Jacksonville: Single mother beats COVID-19 with home health care treatment ActionNewsJax.com
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Jacksonville: Single mother beats COVID-19 with home health care treatment - ActionNewsJax.com
The monthly jobs reportthe unemployment rate from one survey and the change in employer payrolls from another surveyis one of the most closely watched economic indicators, particularly at a time of an economic crisis like today. Heres a look at how these data are collected and how to interpret them during the COVID-19 pandemic.
The headline unemployment rate measures the percentage of people over age 16 who arent working but are actively looking for work. The unemployment rate was hovering around a 50-year low before the pandemic, rose to 4.4% in March, and is certain to have been much higher in April. The Congressional Budget Office projects that the unemployment rate will rise to 16% this summer.
Data on unemployment are collected every month in the Current Population Survey (CPS), a survey of about 60,000 households, conducted by the Census and the Bureau of Labor Statistics every month, which includes roughly 105,000 people ages 16 and older. The questions about unemployment refer to what people were doing during the week that includes the 12th of the month, known as the reference weekso in the case of the survey to be released on Friday (May 8) the week of April 12. The CPS is also referred to as the household survey, to distinguish it from the establishment survey, which is the source for the official employment numbers released each month.
First, they are asked whether they worked during the week of April 12. People are counted as employed if they did any work at all as a paid employee, if they worked in their own business, or worked without pay for at least 15 hours in a family business. People are also counted as employed if they were temporarily absent from work as a result of sickness, bad weather, vacation, a strike, or personal reasons. Such workers are classified as employed but absent from work. People not counted as employed are then asked if they have both looked for work in the previous four weeks and are available to work. If so, they are counted as unemployed. In addition, people who did not work, but are on temporary layoff from a job with the expectation that they will be recalledwhat many furloughed employees are experiencing todayare counted as unemployed whether they looked for a job or not.
People who are not working and who dont meet the criteria to be counted as unemployed for the headline unemployment rate (known as U-3) are counted as being out of the labor force. This category includes students, retirees, and those who stay home to take care of family members. In addition, people who report wanting a job but have not looked for work in the most recent four weeks are considered out of the labor force, but they are not ignored in the official statistics. The Bureau of Labor Statistics (BLS) reports several measures of the labor market beyond the headline unemployment rate. The U-6, for instance, counts all those who are technically unemployed plus those are who are working part-time but would prefer full time work and those marginally attached to the labor force, that is, people who say they want either a full-time or part-time job, have not looked for work in the most recent four weeks, but have looked for a job sometime in the past 12 months. When adults classified as marginally attached report that they did not recently seek work because they do not believe jobs are available for them, they are classified as discouraged workers.
While the survey will be the same as always, the nature of the COVID-19 economy means that peoples behavior, and hence the data, may not follow the same pattern that we usually see when the economy is turning down. Typically, people who lose jobs in recessions are more likely to transition into unemployment than to transition out of the labor force. However, with stay-at-home orders in place and nonessential businesses closed in many communities, people who leave employment now are much less likely to be seeking work than would typically be the case. In addition, schools are closed in many places, which means that many people who lost their jobs have child-care responsibilities that prevent them from seeking or accepting a new job. These dynamics mean that, relative to a typical downturn, we might expect the headline unemployment rate to rise less and the percent of those out of the labor force to raise more, especially the percent of those who say they want a job but arent looking. Indeed, we saw evidence of this in March, when, relative to the prior trend, an additional 1.2 million people moved from employment to out of the labor force, and the number of people categorized as out of the labor force but wanting a job rose by 500,000. The resulting increase in the labor force participation rate was much higher than would be expected given the rise in the unemployment rate.
When people first file for unemployment insurance (UI), they are counted as an initial claim. So when unemployment increases, initial claims tend to rise. Because initial claims are reported weekly, they are often used as an early indicator of the overall unemployment rate.
The number of people receiving UI and the number counted as unemployed do tend to move in the same direction, but there is no formal link between the two indicators. The only criteria for being counted as unemployed (and hence included in the unemployment rate) are that you are without a job and that you have actively searched for work or are on temporary layoff. You need not be collecting unemployment insurance to be counted as unemployed. And some people are eligible to collect partial unemployment insurance benefits if they are working but have been assigned a schedule that is far below their usual weekly hours.
Many people who become unemployed do not apply for UI benefits, either because they are not eligible or because they choose not to apply. So initial claims typically understate the number of people becoming unemployed in a given week. That said, there are people who file an initial claim and are not counted as unemployed in the CPS. This could happen if a person doesnt meet the CPS criteria for being unemployed, for instance if they file for UI because their work schedule was reduced, or if the person has a very short spell of unemployment which is not captured in the CPS (for example a person who becomes unemployed and finds a job in t between survey reference weeks).
Furthermore, many people who are unemployed and do file an initial claim do not end up receiving unemployment insurance benefits, either because they are not covered by the program, because they have not accumulated enough working hours to be eligible for benefits, or because they dont satisfy the job search requirements. In February, before the pandemic, the number of people unemployed was about 5.8 million while the number of people receiving UI benefits averaged only about 1.7 million.
Not necessarily.
The recently enacted CARES Act increased the pool of people eligible for UI benefits and temporarily increased benefits by $600 a week. These changes will affect the usual relationship between the number of people receiving UI and the number of people counted as unemployed in the direction of increasing the share of the unemployed who receive benefits. Specifically, the CARES Act expanded UI eligibility to include the self-employed, contractors, and gig workers. Moreover, the CARES Act enables individuals to collect UI benefits for additional reasons. For instance, individuals may collect UI benefits if they are quarantined with the expectation of returning to work after the quarantine is over or if they leave employment due to a risk of exposure, due to infection, or to care for a family member (either because the family member is sick or because the typical care arrangement has been disrupted by the virus). Although many states appear to be interpreting these changes very narrowly, taken together they nonetheless imply that a greater share of the unemployed will receive UI benefits.
The CARES Act also allows states to waive the requirement that people must search for work to be eligible for benefits. As of early May, 40 states have waived the search requirements under certain circumstances. As a result, more UI beneficiaries than usual wont be actively looking for work, which suggests that fewer UI recipients than usual will be categorized as being unemployed. However, whether they are counted as unemployed or not will depend on what they tell the government survey-taker about their expectation of being recalled. If they expect to be recalled, the CPS considers them on temporary layoff and counts them as unemployed; if they do not expect to be recalled they are counted as out of the labor force. Note that, while the CPS questions are designed to elicit consist responses across survey participants, identically situated individuals could be classified differently depending on how they view their likelihood of being recalled.
A final consideration: the CARES Act does not require a person impacted by COVID-19 to quit their jobs in order to receive benefits. The CPS counts people as employed if they have a job but are absent temporarily due to a variety of circumstances including illness and childcare problems. In March, the BLS instructed survey interviewers to classify employed persons absent from work due to corona virus-related business closures as unemployed or on temporary layoff. However, the BLS itself noted that there were still an unusually large number of individuals classified as employed and temporarily absent. This outcome suggests that the rule will likely reduce the share of people receiving UI benefits who are counted as unemployed.
The bottom line: The number of people receiving UI benefits during the COVID-19 crisis may rise more sharply than the number of people counted as unemployed.
The payroll (or establishment) survey is a survey of 145,000 businessesemploying about one third of all workers on nonfarm payrollsadministered by the BLS. The payroll survey tends to have difficulty estimating employment growth when the economy is at a turning point, as is the case now. To create the sample to be surveyed, the BLS picks firms from the universe of firms that have unemployment insurance tax accounts. However, new firms do not enter the BLS sample universe right away, and the BLS can have difficulty distinguishing a nonresponse from a firm closure in real time. Since the net contribution of jobs created at new firms and jobs destroyed at closing firms is typically small, the BLS imputes the same trend change in employment as occurred at continuing firms to firms that close (or for those who do not respond). It then uses a model, called the net birth-death model, to forecast the residual between that imputation and the actual data. This model tends to overestimate employment growth when the economy is weakening and underestimate it when the economy is improving. And while the model error is typically small, it can, on occasion, be large.
The way that we know about these forecast errors is because in March of each year, the BLS revises the data based on more complete information. These revisions include benchmarking the level of payroll employment to population employment, primarily using the unemployment insurance tax records (published in the BLS Quarterly Census of Employment and Wages), which represent the near-universe of employment for the payroll survey data. Although there are a few factors that can cause the level of payroll employment to deviate from population employment, the failure to adequately account for net births and deaths is likely the most important. In most years the benchmark is small, with the level of employment revising up or down by less than 0.2 percentage points. However, strikingly, when the establishment survey data for March 2009the depths of the Great Recessionwere benchmarked, the level of payroll employment was revised down by over 900,000 jobs, or 0.7 percentage point, meaning that employers had shed an 75,000 more jobs each month between April 2008 and March 2009 than previously estimated.
If, as a result of the pandemic, an unusually large number of firms are closing and few are opening, it seems possible that the even the dramatic decline in employment that we are likely to see will underestimate the true extent of job loss.
When people become unemployed, they lose an important (and sometimes their only) source of income and are at risk of falling into poverty. Of course, the more generous the unemployment insurance, the less likely it is for someone who loses a job to become poor. But unemployment insurance has typically replaced only about 40 percent of lost wages, on average over the past 20 years, with a lot of variation in generosity across the states.
The CARES Act changed that. It boosted the weekly unemployment insurance benefit by $600 through the end of July. This increase will more than double weekly UI benefits except in the case of laid-off workers who earn high wages. This UI benefit hike will go a long way toward preventing eligible families from falling into poverty.
In addition, the federal government is making direct payments of up to $1,200 per adult and $500 per dependent child under the age of 18, with the payments phasing out for those with higher levels of income. It also has encouraged states to request waivers that would allow them to increase SNAP (or food stamps) benefits and suspend time limits for able-bodied adults without children. That said, given the decline in economic activity, many households will still be facing very difficult economic conditions in coming months.
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Making sense of the monthly jobs report during the COVID-19 pandemic - Brookings Institution
The Pharr-San Juan-Alamo school district will be the first in Hidalgo County to host testing sites for COVID-19, according to a district news release Tuesday. The decision was made during a school board meeting Monday.
Testing begins Tuesday at PSJA North Early College High School and will continue until Friday, from 1 to 6 p.m., and will be offered in Pharr, San Juan and Alamo on a rotation basis to minimize the risk of spreading COVID-19. The sites will be organized in a drive-thru fashion.
The next few weeks will include locations such as PSJA Early College High School from May 11-15, PSJA Memorial Early College High School from May 18-22 and PSJA Southwest Early College High School from May 25-29.
The drive-thru testing will be offered to anyone regardless of symptoms or other risk factors.
Uninsured community members will be charged $100 per test while in-network insured members will not have to pay a co-pay.
We are grateful to our school board of trustees for allowing us to utilize our school grounds as a testing service in order to do our part to minimize the spread of this disease in our community, PSJA Superintendent Jorge L. Arredondo said in the release.
For information on pre-screening procedures, visit phsrgv.com/covid19/.
If you have news you would like to contribute, you can reach The Monitor at (956) 683-4000.
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PSJA ISD schools to host drive-thru testing for COVID-19 - Monitor
The World Health Organization says the United States government has not given any evidence to support Trump administration officials speculative claim that Covid-19 originated in a Wuhan lab, as China dismissed the theory as insane.
Donald Trump has repeatedly said that he has proof the virus originated in a laboratory in Wuhan whereas scientists believe it jumped from animals to humans at a wet market in the city last year.
The US secretary of state, Mike Pompeo, said on Sunday that the US had enormous evidence to back the theory. But the administration had not produced it publicly or provided it to the WHO, said its emergencies director, Dr Michael Ryan. So from our perspective, this remains speculative.
Like any evidence-based organisation, we would be very willing to receive any information that purports to the origin of the virus, Ryan said, stressing that this was a very important piece of public health information for future control.
If that data and evidence is available, then it will be for the United States government to decide whether and when it can be shared, but it is difficult for the WHO to operate in an information vacuum in that regard.
Ryan said it was important for the WHO to learn from Chinese scientists data and exchange knowledge to find the answers together, but cautioned against politicising the issue. If this is projected as aggressive investigation of wrongdoing, that is much more difficult to deal with. Thats a political issue, he said.
Chinese state media attacked the US claims, with the state broadcaster CCTV labelling them insane and evasive in a Monday opinion piece entitled Evil Pompeo is wantonly spewing poison and spreading lies. The state-backed Global Times also published an editorial accusing Pompeo and Trump of bluffing, and said if the US had evidence it should present it.
If Washington has solid evidence, then it should let research institutes and scientists examine and verify it, the editorial said. The Global Times editor, Hu Xijin, tweeted that demands to investigate the Wuhan lab were an attempt to fool the American people.
Last week Sky News reported the WHO had been denied any involvement in Chinas investigations into the origins of the virus.
Intelligence sources have told the Guardian there is no current evidence to suggest coronavirus leaked from a Chinese research laboratory. Reports in Australia suggested its intelligence officials believed a dossier touted by the Trump administration to support the laboratory theory was compiled from news reports rather than actual material from the Five Eyes spy network of Australia, the US, New Zealand, Canada and the UK.
The USs senior infectious disease expert, Dr Anthony Fauci, has said the available evidence was very, very strongly leaning toward this [virus] could not have been artificially or deliberately manipulated.
The WHOs technical lead on Covid-19, Dr Maria Van Kerkhove, stressed during Mondays briefing that there were some 15,000 full genome sequences of the novel coronavirus available, and from all of the evidence that we have seen ... this virus is of natural origin.
While coronaviruses generally originate in bats, both Van Kerkhove and Ryan stressed the importance of discovering how the virus that causes Covid-19 crossed over to humans, and what animal served as an intermediary host along the way.
Several nations, including Australia and the UK, have called for an independent investigation into the outbreak, angering China.
Scott Morrison, the prime minister of key US ally, Australia, said on Tuesday it was most likely that the virus originated in a wildlife wet market. Australia is a member of the Five Eyes intelligence network, that includes the US. Morrison reiterated his call for an independent investigation into the viruss origins and said he had written to G20 leaders asking for support for a proper review.
Citing an internal Chinese government report on Tuesday, the Reuters news agency reported international anti-China sentiment was at its highest since the 1989 Tiananmen Square massacre.
According to the report, which Reuters said was presented in early March to top Beijing leaders including the president, Xi Jinping, the global hostility could tip US-China relations into confrontation once the pandemic was over.
On Monday the US deputy national security adviser, Matthew Pottinger, sounded a warning to Beijing and asked in a speech delivered in Mandarin whether China today would benefit from a little less nationalism and a little more populism.
Around the world infection numbers and fatalities have continued to rise. The death toll has passed a quarter of a million globally, and the USs daily toll is projected to double by June to 3,000 according to the New York Times, which cited internal document containing projections based on modelling by the Centers for Disease Control and Prevention and pulled together in chart form by the Federal Emergency Management Agency.
By Tuesday morning the United Kingdom was approaching the death toll of Italy with 28,734 fatalities recorded. So far at least 29,079 people in Italy have died of Covid-19 the second most globally after the US although it is believed by experts that the true death toll is higher. In other developments:
Australia and New Zealand have discussed the prospect of a trans-Tasman bubble allowing travel which has been anticipated by both countries leaders at a meeting of Australias national cabinet meeting, which was joined by Jacinda Ardern.
New Zealand has had a second straight day of no new cases of Covid-19 , as the government considered whether to further relax the countrys lockdown restrictions, due to expire on Monday.
In the UK workers may refuse to turn up or stage walk-outs unless the government helps guarantee their safety, trade unions have warned amid anger over guidance designed to ease the lockdown.
The WHO hailed the billions of euros raised on Monday during a teleconference of world leaders to boost development of a coronavirus vaccine as a strong show of global solidarity.
Some California retailers will be allowed to reopen their businesses starting on Friday, after a six-week stay-at-home order, the states governor, Gavin Newsom, said Monday.
Carnival Cruise Line has announced plans to resume operations at the beginning of August despite dozens of deaths on cruise ships during the Covid-19 pandemic.
A plane carrying aid supplies has crashed in Somalia. The accident, involving an African Express Airways plane, killed seven people on board, a security official said.
Austrian unemployment is at all-time high, with a year-on-year rise of almost 60%
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WHO says it has no evidence to support 'speculative' Covid-19 lab theory - The Guardian
Tracing infection from mobility data
What sort of measures are required to contain the spread of severe acute respiratory syndromecoronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19)? The rich data from the Open COVID-19 Data Working Group include the dates when people first reported symptoms, not just a positive test date. Using these data and real-time travel data from the internet services company Baidu, Kraemer et al. found that mobility statistics offered a precise record of the spread of SARS-CoV-2 among the cities of China at the start of 2020. The frequency of introductions from Wuhan were predictive of the size of the epidemic sparked in other provinces. However, once the virus had escaped Wuhan, strict local control measures such as social isolation and hygiene, rather than long-distance travel restrictions, played the largest part in controlling SARS-CoV-2 spread.
Science, this issue p. 493
The ongoing coronavirus disease 2019 (COVID-19) outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions were undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We used real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation in transmission in cities across China and to ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. After the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside of Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.
The outbreak of coronavirus disease 2019 (COVID-19) spread rapidly from its origin in Wuhan, Hubei Province, China (1). A range of interventions were implemented after the detection in late December 2019 of a cluster of pneumonia cases of unknown etiology and identification of the causative virus, severe acute respiratory syndromecoronavirus 2 (SARS-CoV-2), in early January 2020 (2). Interventions include improved rates of diagnostic testing; clinical management; rapid isolation of suspected cases, confirmed cases, and contacts; and, most notably, restrictions on mobility (hereafter called cordon sanitaire) imposed on Wuhan city on 23 January 2020. Travel restrictions were subsequently imposed on 14 other cities across Hubei Province, and partial movement restrictions were enacted in many cities across China. Initial analysis suggests that the Wuhan cordon sanitaire resulted in an average 3-day delay of COVID-19 spread to other cities (3), but the full extent of the effect of the mobility restrictions and other types of interventions on transmission has not been examined quantitatively (46). Questions remain over how these interventions affected the spread of SARS-CoV-2 to locations outside of Wuhan. Here, we used real-time mobility data, crowdsourced line list data of cases with reported travel history, and timelines of reporting changes to identify early shifts in the epidemiological dynamics of the COVID-19 epidemic in China, from an epidemic driven by frequent importations to local transmission.
As of 1 March 2020, 79,986 cases of COVID-19 were confirmed in China (Fig. 1A) (7). Reports of cases in China were mostly restricted to Hubei until 23 January 2020 (81% of all cases), after which most provinces reported rapid increases in cases (Fig. 1A). We built a line list dataset from reported cases in China with information on travel history and demographic characteristics (8). We note that the majority of early cases (before 23 January 2020; see the materials and methods) reported outside of Wuhan had known travel history to Wuhan (57%) and were distributed across China (Fig. 1B), highlighting the importance of Wuhan as a major source of early cases. However, initial testing was focused mainly on travelers from Wuhan, potentially biasing estimates of travel-related infections upward (see the materials and methods). Among cases known to have traveled from Wuhan before 23 January 2020, the time from symptom onset to confirmation was 6.5 days (SD = 4.2 days; fig. S2), providing opportunity for onward transmission at the destination. More active surveillance reduced this interval to 4.8 days (SD = 3.03 days; fig. S2) for those who traveled after 23 January 2020.
(A) Epidemic curve of the COVID-19 outbreak in provinces in China. Bars indicate key dates: implementation of the cordon sanitaire of Wuhan (gray) and the end of the first incubation period after the travel restrictions (red). The black line represents the closure of the Wuhan seafood market on 1 January 2020. The width of each horizontal tube represents the number of reported cases in that province. (B) Map of COVID-19 confirmed cases (n = 554) that had reported travel history from Wuhan before travel restrictions were implemented on 23 January 2020. Colors of the lines indicate date of travel relative to the date of travel restrictions.
To identify accurately a time frame for evaluating early shifts in SARS-CoV-2 transmission in China, we first estimated from case data the average incubation period of COVID-19 infection [i.e., the duration between time of infection and symptom onset (9, 10)]. Because infection events are typically not observed directly, we estimated the incubation period from the span of exposure during which infection likely occurred. Using detailed information on 38 cases for whom both the dates of entry to and exit from Wuhan were known, we estimated the mean incubation period to be 5.1 days (SD = 3.0 days; fig. S1), similar to previous estimates from other data (11, 12). In subsequent analyses, we added an upper estimate of one incubation period (mean + 1 SD = 8 days) to the date of Wuhan shutdown to delineate the date before which cases recorded in other provinces might represent infections acquired in Hubei (i.e., 1 February 2020; Fig. 1A).
To understand whether the volume of travel within China could predict the epidemic outside of Wuhan, we analyzed real-time human mobility data from Baidu Inc., together with epidemiological data from each province (see the materials and methods). We investigated spatiotemporal disease spread to elucidate the relative contribution of Wuhan to transmission elsewhere and to evaluate how the cordon sanitaire may have affected it.
Among cases reported outside of Hubei province in our dataset, we observed 515 cases with known travel history to Wuhan and a symptom onset date before 31 January 2020, compared with only 39 cases after 31 January 2020, illustrating the effect of travel restrictions (Figs. 1B and 2A and fig. S3). We confirmed the expected decline of importation with real-time human mobility data from Baidu Inc. Movements of individuals out of Wuhan increased in the days before the Lunar New Year and the establishment of the cordon sanitaire, before rapidly decreasing to almost no movement (Fig. 2, A and B). The travel ban appears to have prevented travel into and out of Wuhan around the time of the Lunar New Year celebration (Fig. 2A) and likely reduced further dissemination of SARS-CoV-2 from Wuhan.
(A) Human mobility data extracted in real time from Baidu Inc. Travel restrictions from Wuhan and large-scale control measures started on 23 January 2020. Gray and red lines represent fluxes of human movements for 2019 and 2020, respectively. (B) Relative movements from Wuhan to other provinces in China. (C) Timeline of the correlation between daily incidence in Wuhan and incidence in all other provinces, weighted by human mobility.
To test the contribution of the epidemic in Wuhan to seeding epidemics elsewhere in China, we built a nave COVID-19 generalized linear model [GLM (13)] of daily case counts (see the materials and methods). We estimated the epidemic doubling time outside of Hubei to be 4.0 days (range across provinces, 3.6 to 5.0 days) and estimated the epidemic doubling time within Hubei to be 7.2 days, consistent with previous reports (5, 12, 14, 15). Our model predicted daily case counts across all provinces with relatively high accuracy (as measured with a pseudo-R2 from a negative binomial GLM) throughout early February 2020 and when accounting for human mobility (Fig. 2C and tables S1 and S2), consistent with an exploratory analysis (6).
We found that the magnitude of the early epidemic (total number of cases until 10 February 2020) outside of Wuhan was very well predicted by the volume of human movement out of Wuhan alone (R2 = 0.89 from a log-linear regression using cumulative cases; fig. S8). Therefore, cases exported from Wuhan before the cordon sanitaire appear to have contributed to initiating local chains of transmission, both in neighboring provinces (e.g., Henan) and in more distant provinces (e.g., Guangdong and Zhejiang) (Figs. 1A and 2B). Further, the frequency of introductions from Wuhan were also predictive of the size of the early epidemic in other provinces (controlling for population size) and thus the probability of large outbreaks (fig. S8).
After 1 February 2020 (corresponding to one mean + one SD incubation period after the cordon sanitaire and other interventions were implemented), the correlation of daily case counts and human mobility from Wuhan decreased (Fig. 2C), indicating that variability among locations in daily case counts was better explained by factors unrelated to human mobility, such as local public health response. This suggests that whereas travel restrictions may have reduced the flow of case importations from Wuhan, other local mitigation strategies aimed at halting local transmission increased in importance later.
We also estimated the growth rates of the epidemic in all other provinces (see the materials and methods). We found that all provinces outside of Hubei experienced faster growth rates between 9 January and 22 January 2020 (Fig. 3, A and B, and fig. S4b), which was the time before travel restrictions and substantial control measures were implemented (Fig. 3C and fig. S6); this was also apparent from the case counts by province (fig. S6). In the same period, variation in the growth rates is almost entirely explained by human movements from Wuhan (Fig. 3C and fig. S9), consistent with the theory of infectious disease spread in highly coupled metapopulations (16, 17). After the implementation of drastic control measures across the country, growth rates became negative (Fig. 3B), indicating that transmission was successfully mitigated. The correlation of growth rates and human mobility from Wuhan became negative; that is, provinces with larger mobility from Wuhan before the cordon sanitaire (but also larger number of cases overall) had more rapidly declining growth rates of daily case counts. This could be due partly to travel restrictions but also to the fact that control measures may have been more drastic in locations with larger outbreaks driven by local transmission (for more details, see Current role of imported cases in Chinese provinces section).
(A) Daily counts of cases in China. (B) Time series of province-level growth rates of the COVID-19 epidemic in China. Estimates of the growth rate were obtained by performing a time-series analysis using a mixed-effects model of lagged, log linear daily case counts in each province (see the materials and methods). Above the red line are positive growth rates and below are negative rates. Blue indicates dates before the implementation of the cordon sanitaire and green after. (C) Relationship between growth rate and human mobility at different times of the epidemic. Blue indicates before the implementation of the cordon sanitaire and green after.
The travel ban coincided with increased testing capacity across provinces in China. Therefore, an alternative hypothesis is that the observed epidemiological patterns outside of Wuhan were the result of increased testing capacity. We tested this hypothesis by including differences in testing capacity before and after the rollout of large-scale testing in China on 20 January 2020 [the date that COVID-19 became a class B notifiable disease (18, 19)] and determined the impact of this binary variable on the predictability of daily cases (see the materials and methods). We plotted the relative improvement in the prediction of our model (on the basis of normalized residual error) of (i) a model that includes daily mobility from Wuhan and (ii) a model that includes testing availability (for more details, see the materials and methods). Overall, the inclusion of mobility data from Wuhan produced an improvement in the models prediction [delta-Bayesian information criterion > 250 (20)] over a nave model that considers only autochthonous transmission with a doubling time of 2 to 8 days (Fig. 3B). Of the 27 provinces in China reporting cases through 6 February 2020, we found that the largest improvements in prediction for 12 provinces could be achieved using mobility only (fig. S5). In 10 provinces, both testing and mobility improved the models prediction, and in only one province (Hunan) was testing the most important factor improving model prediction (fig. S5). We conclude that laboratory testing during the early phase of the epidemic was critical; however, mobility out of Wuhan remained the main driver of spread before the cordon sanitaire. Large-scale molecular and serological data will be important to investigate further the exact magnitude of the impact of human mobility compared with other factors.
Because case counts outside of Wuhan have decreased (Fig. 3B), we can further investigate the current contribution of imported cases to local epidemics outside of Wuhan by investigating case characteristics. Age and sex distributions can reflect heterogeneities in the risk of infection within affected populations. To investigate meaningful shifts in the epidemiology of the COVID-19 outbreak through time, we examined age and sex data for cases from different periods of the outbreak and from individuals with and without travel from Wuhan. However, details of travel history exist for only a fraction of confirmed cases, and this information was particularly scant for some provinces (e.g., Zhejiang and Guangdong). Therefore, we grouped confirmed cases into four categories: (I) early cases (i.e., reported before 1 February 2020) with travel history, (II) early cases without travel history, (III) later cases (i.e., reported between 1 February and 10 February 2020) with travel history, and (IV) later cases without travel history.
Using crowdsourced case data, we found that cases with travel history (categories I and III) had similar median ages and sex ratios in both the early and later phases of the outbreak (age 41 versus 42 years; 50% interquartile interval: 32.75 versus 30.75 and 54.25 versus 53.5 years, respectively; P value > 0.1, 1.47 versus 1.45 males per female, respectively; Fig. 4D and fig. S7). Early cases with no information on travel history (category II) had a median age and sex ratio similar to those with known travel history (age 42 years; 50% interquartile interval: 30.5 to 49.5, P value > 0.1; 1.80 males per female; Fig. 4D). However, the sex ratio of later cases without reported travel history (category IV) shifted to ~1:1 (57 male versus 62 female, 2 test, P value < 0.01), as expected under a null hypothesis of equal transmission risk [Fig. 4, A, B, and D; see also (21, 22) and the materials and methods], and the median age in this group increased to 46 (50% interquartile interval: 34.25 to 58, t test: P value < 0.01; Fig. 4, A to C, and fig. S7). We hypothesize that many of the cases with no known travel history in the early phase were indeed travelers who contributed to disseminating SARS-CoV-2 outside of Wuhan. The shift toward more equal sex ratios and older ages in nontravelers after 31 January 2020 confirms the finding that epidemics outside of Wuhan were then driven by local transmission dynamics. The case definition changed to include cases without travel history to Wuhan after 23 January 2020 (see the materials and methods).
(A) Age and sex distributions of confirmed cases with known travel history to Wuhan. (B) Age and sex distributions of confirmed cases that had no travel history to Wuhan. (C) Median age for cases reported early (before 1 February) and those reported later (between 1 and 10 February). Full distributions are shown in fig. S7. (D) Change through time in the sex ratio of (i) all reported cases in China with no reported travel history, (ii) cases reported in Beijing without travel history, and (iii) cases known to have traveled from Wuhan.
Containment of respiratory infections is particularly difficult if they are characterized by relatively mild symptoms or transmission before the onset of symptoms (23, 24). Intensive control measures, including travel restrictions, have been implemented to limit the spread of COVID-19 in China. Here, we show that travel restrictions are particularly useful in the early stage of an outbreak when it is confined to a certain area that acts as a major source. However, travel restrictions may be less effective once the outbreak is more widespread. The combination of interventions implemented in China was clearly successful in mitigating spread and reducing local transmission of COVID-19, although in this work it was not possible to definitively determine the impact of each intervention. Much further work is required to determine how to balance optimally the expected positive effect on public health with the negative impact on freedom of movement, the economy, and society at large.
T. J. Hastie, D. Pregibon, Generalized linear models in Statistical Models in S, J. M. Chambers, T. J. Hastie, Eds. (Wadsworth & Brooks/Cole, 1992), pp. 195246.
M. J. Keeling, O. N. Bjrnstad, B. T. Grenfell, Metapopulation dynamics of infectious diseases in Ecology, Genetics and Evolution of Metapopulations, I. Hanski, O. E. Gaggiotti, Eds. (Elsevier, 2004), pp. 415445.
J. H. McDonald, Handbook of Biological Statistics (Sparky House, ed. 3, 2014).
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Experts say the only way to defeat the current coronavirus pandemic is with comprehensive testing and tracing to identify infected persons and ensure they dont come into contact with others. To achieve this, the Army wants to fast-track development of wearable detection technologies that can predict infection and alert the wearer and others around them.
On Monday, the Army issued a request for project proposals through the Medical Technology Enterprise Consortium to develop a wearable diagnostic capability for the pre/very earlysymptomatic detection of COVID19 infection.
Using its other transaction authorityan iterative, fast-paced funding mechanism outside the Federal Acquisition Regulationthe Defense Department set aside $25 million for this effort, with plans to make up to 10 awards.
Physiologic surveillance for COVID19 positive individuals that do not yet show clear medical symptoms is an ultimate goal, the solicitation states, citing the need for advanced algorithms to predict infection. Physiological signatures therefore must produce predictive algorithms that can be tied into validated and relevant antibody/molecular measurements.
The perfect solution will be designed so the devices outputa determination on whether the person poses an infection riskcan be easily decipherable by non-medical or -technical personnel and to be minimally-invasive for the wearer.
Device(s) should be designed to be worn for continuous physiological monitoring in a non-obtrusive manner and should not affect the daily activity of the wearer, the solicitation states.
Physiological indicators should include, but are not limited to, physiological markers of early COVID symptomologyelevated temperature/fever, respiratory difficulty/cough, etc.antibodies against COVID-19, and molecular biomarkers indicative of COVID-19 exposure.
The data generate and transmitted by the device must also be secured at the highest level, per regulations under the Health Insurance Portability and Accountability Act, or HIPAA.
Army contracting officials said the military would prefer a single device that meets all needs but will accept pitches for a combination of technologies and sensors.
The winning bidders should also be prepared to work through any necessary FDA approvals required, including obtaining an Emergency Use Authorization within the first 45 days of the contract.
The Army is not looking for the latest, undeveloped tech. Instead, the requirement calls for all submissions to be at least at Technology Readiness Level 3 or 4working proof-of-concept or tested in a laboratory environmentor above. More specifically, proposed technologies should currently be in development or commercially available, according to the solicitation.
MTEC said the contract will be awarded on an accelerated timeline through its Enhanced White Paper method, with awards expected within four weeks from the white paper deadline: 12 p.m. May 13.
Due to the urgent need to deploy this technology mid-crisis, MTEC is also suspending its members-only submission requirement. As with other OTA consortia, MTEC usually restricts access to the requirements and submissions to its membership.
Due to the critical and urgent nature of the technical topic area, MTEC membership is NOT required for the submission of an Enhanced White Paper in response to this MTEC RPP, the solicitation states. However, vendors will be required to join the consortium if their white paper is chosen to move forward to award.
The contract is set to run for nine months.
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More than240,000 people worldwide have already died of Covid-19, and before the pandemic finishes, it could kill hundreds of thousands, even millions, more. But the final toll is destined to be far higher than just those who die of Covid-19. Experts warn that deaths from secondary impacts poverty, hunger, diseases, and violence exacerbated by the pandemic may dwarf the number of those who die of the coronavirus itself.
A new analysis by researchers from Kings College London and Australian National University, under the aegis of the United Nations University World Institute for Development Economics Research, for example, warns that the economic contraction caused by Covid-19 could push an additional 500 million people about eight percent of the earths population into poverty, reversing 30 years of economic improvement. We were surprised at the sheer scale of the potential poverty tsunami that could follow Covid-19 in developing countries, said Andy Sumner, one of the studys authors.
Not surprisingly, such financial fallout has grim knock-on effects. I want to stress that we are not only facing a global health pandemic but also a global humanitarian catastrophe,warned David Beasley, the executive director of the United Nations World Food Program. Millions of civilians living in conflict-scarred nations, including many women and children, face being pushed to the brink of starvation, with the specter of famine a very real and dangerous possibility. A new study by the WFP found that lockdowns and the economic recession caused by Covid-19 may exacerbate an already dire worldwide hunger crisis, almost doubling the number of people who could go hungry, pushing a total of 265 million people to the brink of starvation by the end of the year.
Meanwhile, the World Health Organizationnotes that a diversion of resources could have especially devastating effects on the fight against malaria. Under a worst-case scenario, in which all insecticide-treated bed net campaigns are suspended and there is a 75% reduction in access to effective antimalarial medicines, fatalities from the mosquito-borne illness could reach 769,000 double the number of deaths in 2018 effectively wiping out 20 years of gains in suppressing malaria mortality. Similarly, a new analysis by researchers at Imperial College London found that in low- and middle-income countries, disruptions to health services could cause deaths from HIV, tuberculosis and malaria to increase by up to 10, 20, and 36 percent respectively over five years.
As Covid-19 cases surge worldwide, the survival of pregnant women and children is at great risk due to strained healthcare systems, and the disruption of life-saving health services, said Dr. Stefan Peterson, associate director and global chief of health at the United Nations International Childrens Emergency Fund. In fact, researchers at the Johns Hopkins Bloomberg School of Public Health warn, for example, that the impact of the pandemic to newborn, child and adolescent health and nutrition services might lead to the deaths of 1.2 million children a 45 percent increase over existing child mortality levels.
The overall death toll among the young may actually be exponentially higher according to a recent report from the Christian aid organization, World Vision. An analysis of 24 countries with existing humanitarian crises from Afghanistan to Yemen found that as many as 30 million childrens lives are at risk from other diseases, like diphtheria, whooping cough, and tetanus, if healthcare systems are swamped by the pandemic and resources are diverted from immunizations. We are wrong if we think this is not a childrens disease, said Andrew Morley, World Visions president and chief executive. Experience tells us that when epidemics overwhelm health systems, the impact on children is deadly.
Among youths, girls will suffer most according to a recent analysis by Plan International, an aid organization that advocates for childrens rights and equality for girls. Since women and girls undertake more than three quarters of unpaid care and, in rural communities and low-income countries, spend up to 14 hours a day on such work, girls will likely be at greater risk of infection. But this is just one type of collateral damage. Covid-19 shutdowns will disrupt early learning, formal education and livelihoods, according to the report. Measures to curb the disease have worsened existing inequalities, forcing girls out of school and placing them at heightened risk of violence in their home.
UNHCR, the United Nations refugee agency, notes that even before the Covid-19 pandemic, an estimated one in three women had experienced physical or sexual abuse. Confinement, lockdowns, and quarantines coupled with deteriorating socioeconomic conditions have now created a perfect storm. These factors significantly increase the risks of intimate partner violence, with refugees, internally displaced and stateless persons among the most vulnerable, according to the agency. But the shadow pandemic of violence against girls and women extends far beyond refugees and displaced war victims, with reports of domestic violence having increased by 30% in France while calls to helplines have jumped 30% in Cyprus, and 33% in Singapore.
The United Nations study forecast that the greatest economic impact of Covid-19 will be in sub-Saharan Africa, where cases are rapidly increasing and, if projections prove accurate, up to half of the new poor will live. There, the effects will be felt in infant and maternal mortality, undernutrition, malnourishment, and educational achievement, among other indicators.
Alexandra Lamarche, senior advocate for West and Central Africa at Refugees International, explained that preventative measures aimed at countering Covid-19 were also impediments to humanitarian aid, leaving poor people without access to food. Were going to see significant impacts on malnutrition rates, she told The Intercept. And were seeing all sorts of secondary impacts. For example, theres a polio outbreak in Niger because they stopped vaccinating. Between hunger and disease, poverty and violence, the follow-on effects of Covid-19 threaten to be as wide-ranging as they are lethal. There are just so many different unexpected consequences, said Lamarche. Its extremely disheartening. And its going to be exceptionally dire.
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Global Impact of Covid-19 May Include More Poverty, Hunger - The Intercept
How safe is swimming during the COVID-19 coronavirus pandemic? (Photo: Getty)
It looks like the COVID-19 coronavirus may be able to live in water for a few days, potentially even a few weeks. There is a big but, though. And youll like this big but. Just because a virus can survive in water doesnt necessarily mean that its present in large enough concentrations to infect you.
Is this situation a bit like a teenager pointing out a few hairs on his face and then claiming that its a beard? There actually has to be enough hairs to make it a beard. When you can still count the number of hairs, its not a beard, unless, of course, the hairs are really, really long and very, very curly.
Similarly, consider what is known about the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in water. Indeed studies have suggested that the SARS-CoV2 could actually hang out in the wet stuff for a little while. For example, a study published in the journal Water Research in 2009 found that two viruses that have similarities to the original SARS virus, the transmissible gastroenteritis (TGEV) and mouse hepatitis (MHV) viruses, could survive up to days and even weeks in water. The University of North Carolina team (LisaCasanova, William A.Rutal, David J.Weber, and Mark D.Sobsey) that conducted the study concluded that coronaviruses can remain infectious for long periods in water and pasteurized settled sewage, suggesting contaminated water is a potential vehicle for human exposure if aerosols are generated.
Pictured here are sewer tunnels underneath the streets of Paris, France. (Photo: Getty)
Then there was the poopy study described by a paper posted April 17 on medRxiv. For this study, the team sampled sewage (you know, the watery stuff in sewers) in the greater Paris, France, area for over a month. They found that concentrations of the SARS-CoV2 correlated with the number of COVID-19 cases in the region over time. In other words, when COVID-19 cases were rising, so did the concentrations of the SARS-CoV2 in the sewage. This seems like one more reason why splashing sewage or taking a deep breath near sewage is probably not a great idea.
Take this second study with a grain of sewage though. It has not yet been published in a peer-reviewed scientific journal. That means real scientific experts havent had a chance to review the study for quality or accuracy. Telling people that youve posted something on medRxiv can be a bit like telling people that youve auditioned for Americas Got Talent. Theres no guarantee that this study will ever make it close to the final stage of getting published in a reputable peer-reviewed scientific journal.
Regardless, the results from both studies do suggest that the virus can survive for a little while in water, which initially may cause you to wet yourself. Before you do, heres the big but again. Neither study showed that you can actually get infected with the COVID-19 coronavirus from water under the conditions that youd normally be exposed to water. That means via drinking (assuming that you arent drinking sewage or some other type of dirty water), showering, or swimming (assuming that you dont swim in sewage.)
The CDC and EPA have emphasized the safety of drinking water supplies. (Photo: Getty)
In fact, according to the Centers for Disease Control and Prevention (CDC), the COVID-19 coronavirus hasnt even been found in drinking water. And the U.S. Environmental Protection Agency (EPA) has said that the risk to water supplies is low. Americans can continue to use and drink water from their tap as usual. Such a virus would have to get through all of the filtration and water treatments that drinking water typically goes through, and that can be harder than getting on to the red carpet at the Oscars.
Moreover, the great thing about water is that its water. It tends to dilute things. Even if the COVID-19 coronavirus were to somehow make the epic journey of getting into your drinking water, it may not be at high enough concentrations to be of risk to you. This goes back to the whole beard thing. Every virus has a minimum infectious dose, the amount of virus that needs to be present to cause illness. Although its not completely clear yet what the minimum infectious dose for SARS-CoV-2 may be, dilution makes it less likely that what reaches you can surpass this threshold.
The same probably goes for water in pools and hot tubs. The CDC indicates that there is no evidence that the virus that causes COVID-19 can be spread to people through the water in pools, hot tubs, spas, or water play areas. For these things, not only would the water dilute the virus, but also disinfection with chlorine and bromine would likely inactivate the virus.
If you are actually thinking of swimming or soaking in a pool or tub that is not properly chlorinated or bromated, dont. Just dont. Theres a whole lot of other nasty, disease-causing microbes that could then be swimming or soaking along with you. The poop about many swimming pools and hot tubs is thats what people may do in them. As I have described previously for Forbes, 24% of respondents to the2019 Healthy Pools survey indicated that they would enter a swimming pool "within one hour of having diarrhea." Yes, diarrhea. Yes, within one hour of having it. And those are just the people who admitted to doing this. Still dont want to social distance from others?
As for the ocean, it is pretty big. Then theres the motion of the ocean, so to speak. Both of these aspects can dilute and separate viruses fairly quickly. The salt in the water may decrease the survival of the virus as well.
This doesnt mean that you should rush to the beach to do whats seen in this Reuters video:
Sun of a beach. Does this look like social distancing to you? Is everyone in the video staying six feet away from each other? Six feet apart means roughly one Denzel Washington apart, since Washington is about six feet tall. See any people less than one Denzel apart?
When it comes to the COVID-19 coronavirus, the riskiest thing at swimming pools, hot tubs, and oceans is not the water itself. No, its the coughing, sneezing, panting, face-rubbing, and diarrhea-ing things that are in or next to the water: people. Its also the things that people touch frequently such as guard rails, chairs, towels, and thongs.
Do not do this. Not as long as the COVID-19 coronavirus is circulating. (Photo: Getty_
So, the key once again will be doing what you should be doing on land: practicing good social distancing, good hand hygiene, good disinfecting (of objects), and good avoid-touching-your-enormous face. And if you see any random objects such as a sign post, a statue, or a thong, dont touch it if you dont have to do so. You dont know where it has been. Actually, in the case of a thong, you know exactly where its been. Thats the problem.
Also, wait until beaches, swimming pools, and other water areas are officially open before going to them. Yes, staying inside is not easy. Yes, there are only so many episodes of Breaking Bad that you can watch or mind games that you can play with your cat. But patience now will pay off later.
Once such places are officially open, it wont be the time to release the kraken, so to speak. Sure you may have stored up all this energy, all those ingenious pick-up lines that probably wont work anyway, and all the urges to do what you have done before while cooped up inside. But (theres that word again), try to remember that the virus is still circulating. It will be some time before it is water under the bridge.
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Can COVID-19 Coronavirus Live In Water? What About Drinking Water And Swimming - Forbes
Amid Ongoing COVID-19 Pandemic, Governor Cuomo Announces Results of Completed Antibody Testing Study of 15,000 People Showing 12.3 Percent of Population Has COVID-19 Antibodies | Governor Andrew M. Cuomo Skip to main content
State Will Distribute Over7 Million More Cloth Masks to Vulnerable New Yorkers and Frontline Workers Across the State
State is Distributing $25 Million to Food Banks Across the State Through the Nourish New York Initiative
Confirms 4,663 Additional Coronavirus Cases in New York State - Bringing Statewide Total to 312,977; New Cases in 44 Counties
Amid the ongoing COVID-19 pandemic, Governor Andrew M. Cuomo today announced the results of the state's completed antibody testing study, showing 12.3 percent of the population have COVID-19 antibodies. The survey developed a baseline infection rate by testing 15,000 peopleat grocery stores and community centers across the state over the past two weeks.Of those tested, 11.5% of women tested positive and 13.1% of men tested positive. A regional breakdown of the results is below:
Region
Percent Positive
Capital District
2.2%
Central NY
1.9%
Finger Lakes
2.6%
Hudson Valley(Without Westchester/Rockland)
3%
Long Island
11.4%
Mohawk Valley
2.7%
North Country
1.2%
NYC
19.9%
Southern Tier
2.4%
Westchester/Rockland
13.8%
Western NY
6%
Audio Photos
The Governor also announced that the state will distribute over seven million more cloth masks to vulnerable New Yorkers and essential workers across the state. The masks will be distributed as follows:
While we're in uncharted waters it doesn't mean we proceed blindly, and the results of the 15,000 people tested in our antibody survey program - thelargest survey in the nation - will inform our strategy moving forward
The Governor also announced the state is distributing $25 million to food banks across the state through the Nourish New York Initiative. The Nourish New York initiative, announced earlier this week by Governor Cuomo, is working to quickly reroute NewYork's surplus agricultural products to the populations who need them most through New York's network of food banks. Funding will be distributed as follows:
"While we're in uncharted waters it doesn't mean we proceed blindly, and the results of the 15,000 people tested in our antibody survey program - thelargest survey in the nation - will inform our strategy moving forward,"Governor Cuomo said."We're also going to undertake a full survey of antibody testing for transit workers, who have been on the front lines of this crisis. We've said thank you to our essential workers thousands of times but actions speak louder than words, and we want them to know that we're doing everything we can do to keep them safe."
Finally, the Governor confirmed 4,663 additional cases of novel coronavirus, bringing the statewide total to 312,977 confirmed cases in New York State. Of the 312,977 total individuals who tested positive for the virus, the geographic breakdown is as follows:
County
Total Positive
New Positive
Albany
1,238
34
Allegany
35
0
Broome
305
6
Cattaraugus
50
1
Cayuga
51
0
Chautauqua
35
0
Chemung
124
1
Chenango
99
0
Clinton
62
1
Columbia
205
3
Cortland
28
0
Delaware
61
0
Dutchess
3,049
47
Erie
3,598
117
Essex
28
0
Franklin
15
0
Fulton
79
4
Genesee
155
1
Greene
142
3
Hamilton
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The coronavirus has killed so many people in Iran that the country has resorted to mass burials, but in neighboring Iraq, the body count is fewer than 100.
The Dominican Republic has reported nearly 7,600 cases of the virus. Just across the border, Haiti has recorded about 85.
In Indonesia, thousands are believed to have died of the coronavirus. In nearby Malaysia, a strict lockdown has kept fatalities to about 100.
The coronavirus has touched almost every country on earth, but its impact has seemed capricious. Global metropolises like New York, Paris and London have been devastated, while teeming cities like Bangkok, Baghdad, New Delhi and Lagos have, so far, largely been spared.
The question of why the virus has overwhelmed some places and left others relatively untouched is a puzzle that has spawned numerous theories and speculations but no definitive answers. That knowledge could have profound implications for how countries respond to the virus, for determining who is at risk and for knowing when its safe to go out again.
There are already hundreds of studies underway around the world looking into how demographics, pre-existing conditions and genetics might affect the wide variation in impact.
Doctors in Saudi Arabia are studying whether genetic differences may help explain varying levels of severity in Covid-19 cases among Saudi Arabs, while scientists in Brazil are looking into the relationship between genetics and Covid-19 complications. Teams in multiple countries are studying if common hypertension medications might worsen the diseases severity and whether a particular tuberculosis vaccine might do the opposite.
Many developing nations with hot climates and young populations have escaped the worst, suggesting that temperature and demographics could be factors. But countries like Peru, Indonesia and Brazil, tropical countries in the throes of growing epidemics, throw cold water on that idea.
Draconian social-distancing and early lockdown measures have clearly been effective, but Myanmar and Cambodia did neither and have reported few cases.
One theory that is unproven but impossible to refute: maybe the virus just hasnt gotten to those countries yet. Russia and Turkey appeared to be fine until, suddenly, they were not.
Time may still prove the greatest equalizer: The Spanish flu that broke out in the United States in 1918 seemed to die down during the summer only to come roaring back with a deadlier strain in the fall, and a third wave the following year. It eventually reached far-flung places like islands in Alaska and the South Pacific and infected a third of the worlds population.
We are really early in this disease, said Dr. Ashish Jha, the director of the Harvard Global Health Research Institute. If this were a baseball game, it would be the second inning and theres no reason to think that by the ninth inning the rest of the world that looks now like it hasnt been affected wont become like other places.
Doctors who study infectious diseases around the world say they do not have enough data yet to get a full epidemiological picture, and that gaps in information in many countries make it dangerous to draw conclusions. Testing is woeful in many places, leading to vast underestimates of the viruss progress, and deaths are almost certainly undercounted.
Still, the broad patterns are clear. Even in places with abysmal record-keeping and broken health systems, mass burials or hospitals turning away sick people by the thousands would be hard to miss, and a number of places are just not seeing them at least not yet.
Interviews with more than two dozen infectious disease experts, health officials, epidemiologists and academics around the globe suggest four main factors that could help explain where the virus thrives and where it doesnt: demographics, culture, environment and the speed of government responses.
Each possible explanation comes with considerable caveats and confounding counter-evidence. If an aging population is the most vulnerable, for instance, Japan should be at the top of the list. It is far from it. Nonetheless these are the factors that experts find the most persuasive.
Many countries that have escaped mass epidemics have relatively younger populations.
Young people are more likely to contract mild or asymptomatic cases that are less transmissible to others, said Robert Bollinger, a professor of infectious diseases at the Johns Hopkins School of Medicine. And they are less likely to have certain health problems that can make Covid-19, the disease caused by the coronavirus, particularly deadly, according to the World Health Organization.
Africa with about 45,000 reported cases, a tiny fraction of its 1.3 billion people is the worlds youngest continent, with more than 60 percent of its population under age 25. In Thailand and Najaf, Iraq, local health officials found that the 20-to-29 age group had the highest rate of infection but often showed few symptoms.
By contrast, the national median age in Italy, one of the hardest hit countries, is more than 45. The average age of those who died of Covid-19 there was around 80.
Younger people tend to have stronger immune systems, which can result in milder symptoms, said Josip Car, an expert in population and global health at Nanyang Technological University in Singapore.
In Singapore and Saudi Arabia, for instance, most of the infections are among foreign migrant workers, many of them living in cramped dormitories. However, many of those workers are young and fit, and have not required hospitalization.
Along with youth, relative good health can lessen the impact of the virus among those who are infected, while certain pre-existing conditions notably hypertension, diabetes and obesity can worsen the severity, researchers in the United States say.
There are notable exceptions to the demographic theory. Japan, with the worlds oldest average population, has recorded fewer than 520 deaths, although its caseload has risen with increased testing.
And Dr. Jha of Harvard warns that some young people who are not showing symptoms are also highly contagious for reasons that are not well understood.
Cultural factors, like the social distancing that is built into certain societies, may give some countries more protection, epidemiologists said.
In Thailand and India, where virus numbers are relatively low, people greet each other at a distance, with palms joined together as in prayer. In Japan and South Korea, people bow, and long before the coronavirus arrived, they tended to wear face masks when feeling unwell.
In much of the developing world, the custom of caring for the elderly at home leads to fewer nursing homes, which have been tinder for tragic outbreaks in the West.
However, there are notable exceptions to the cultural distancing theory. In many parts of the Middle East, such as Iraq and the Persian Gulf countries, men often embrace or shake hands on meeting, yet most are not getting sick.
What might be called national distancing has also proven advantageous. Countries that are relatively isolated have reaped health benefits from their seclusion.
Far-flung nations, such as some in the South Pacific and parts of sub-Saharan Africa, have not been as inundated with visitors bringing the virus with them. Health experts in Africa cite limited travel from abroad as perhaps the main reason for the continents relatively low infection rate.
Countries that are less accessible for political reasons, like Venezuela, or because of conflict, like Syria and Libya, have also been somewhat shielded by the lack of travelers, as have countries like Lebanon and Iraq, which have endured widespread protests in recent months.
The lack of public transportation in developing countries may have also reduced the spread of the virus there.
The geography of the outbreak which spread rapidly during the winter in temperate zone countries like Italy and the United States and was virtually unseen in warmer countries such as Chad or Guyana seemed to suggest that the virus did not take well to heat. Other coronaviruses, such as ones that cause the common cold, are less contagious in warmer, moist climates.
But researchers say the idea that hot weather alone can repel the virus is wishful thinking.
Some of the worst outbreaks in the developing world have been in places like the Amazonas region of Brazil, as tropical a place as any.
The best guess is that summer conditions will help but are unlikely by themselves to lead to significant slowing of growth or to a decline in cases, said Marc Lipsitch, the director of the Center for Communicable Disease Dynamics at Harvard University.
The virus that causes Covid-19 appears to be so contagious as to mitigate any beneficial effect of heat and humidity, said Dr. Raul Rabadan, a computational biologist at Columbia University.
But other aspects of warm climates, like people spending more time outside, could help.
People living indoors within enclosed environments may promote virus recirculation, increasing the chance of contracting the disease, said Mr. Car of Nanyang Technological University.
The ultraviolet rays of direct sunlight inhibit this coronavirus, according to a study by ecological modelers at the University of Connecticut. So surfaces in sunny places may be less likely to remain contaminated, but transmission usually occurs through contact with an infected person, not by touching a surface.
No scientist has proposed that beaming light inside an infected person, as President Trump has suggested, would be an effective cure. And tropical conditions may have even lulled some people into a false sense of security.
People were saying Its hot here, nothing will happen to me, said Dr. Domnica Cevallos, a medical investigator in Ecuador. Some were even going out on purpose to sunbathe, thinking it would protect them from infection.
Countries that locked down early, like Vietnam and Greece, have been able to avoid out-of-control contagions, evidence of the power of strict social distancing and quarantines to contain the virus.
In Africa, countries with bitter experience with killers like H.I.V., drug-resistant tuberculosis and Ebola knew the drill and reacted quickly.
Airport staff from Sierra Leone to Uganda were taking temperatures (since found to be a less effective measure) and contact details and wearing masks long before their counterparts in the United States and Europe took such precautions.
Senegal and Rwanda closed their borders and announced curfews when they still had very few cases. Health ministries began contact tracing early.
All this happened in a region where health ministries had come to rely on money, personnel and supplies from foreign donors, many of which had to turn their attention to outbreaks in their own countries, said Catherine Kyobutungi, executive director of the African Population and Health Research Center.
Countries wake up one day and theyre like, OK, the weight of the country rests on our shoulders, so we need to step up, she said. And they have. Some of the responses have been beautiful to behold, honestly.
Sierra Leone repurposed disease-tracking protocols that had been established in the wake of the Ebola outbreak in 2014, in which almost 4,000 people died there. The government set up emergency operations centers in every district and recruited 14,000 community health workers, 1,500 of whom are being trained as contact tracers, even though Sierra Leone has only about 155 confirmed cases.
It is not clear, however, who will pay for their salaries or for expenses like motorcycles and raincoats to keep them operating during the coming wet season.
Uganda, which also suffered during the Ebola contagion, quickly quarantined travelers from Dubai after the first case of coronavirus arrived from there. Authorities also tracked down about 800 others who had traveled from Dubai in previous weeks.
The Ugandan health authorities are also testing around 1,000 truck drivers a day. But many of those who test positive have come from Tanzania and Kenya, countries that are not monitoring as aggressively, leading to worries that the virus will keep penetrating porous borders.
Lockdowns, with bans on religious conclaves and spectator sporting events, clearly work, the World Health Organization says. More than a month after closing national borders, schools and most businesses, countries from Thailand to Jordan have seen new infections drop.
In the Middle East, the widespread shuttering of mosques, shrines and churches happened relatively early and probably helped stem the spread in many countries.
A notable exception was Iran, which did not close some of its largest shrines until March 18, a full month after it registered its first case in the pilgrimage city of Qum. The epidemic spread quickly from there, killing thousands in the country and spreading the virus across borders as pilgrims returned home.
As effective as lockdowns are, in countries lacking a strong social safety net and those where most people work in the informal economy, orders closing businesses and requiring people to shelter in place will be difficult to maintain for long. When people are forced to choose between social distancing and feeding their families, they are choosing the latter.
Counter-intuitively, some countries where authorities reacted late and with spotty enforcement of lockdowns appear to have been spared. Cambodia and Laos both had brief spates of infections when few social distancing measures were in place but neither has recorded a new case in about three weeks.
Lebanon, whose Muslim and Christian citizens often go on pilgrimages respectively to Iran and Italy, places rife with the virus, should have had high numbers of infections. It has not.
We just didnt see what we were expecting, said Dr. Roy Nasnas, an infectious disease consultant at the University Hospital Geitaoui in Beirut. We dont know why.
Finally, most experts agree that there may be no single reason for some countries to be hit and others missed. The answer is likely to be some combination of the above factors, as well as one other mentioned by researchers: sheer luck.
Countries with the same culture and climate could have vastly different outcomes if one infected person attends a crowded social occasion, turning it into what researchers call a super-spreader event.
Because an infected person may not experience symptoms for a week or more, if at all, the disease spreads under the radar, exponentially and seemingly at random. Had the woman in Daegu stayed home that Sunday in February, the outbreak in South Korea might have been less than half of what it is.
Some countries that should have been inundated are not, leaving researchers scratching their heads.
Thailand reported the first confirmed case of coronavirus outside of China in mid-January, from a traveler from Wuhan, the Chinese city where the pandemic is thought to have begun. In those critical weeks, Thailand continued to welcome an influx of Chinese visitors. For some reason, these tourists did not set off exponential local transmission.
And when countries do all the wrong things and still end up seemingly not as battered by the virus as one would expect, go figure.
In Indonesia, we have a health minister who believes you can pray away Covid, and we have too little testing, said Dr. Pandu Riono, an infectious disease specialist at the University of Indonesia. But we are lucky we have so many islands in our country that limit travel and maybe infection.
Theres nothing else were doing right, he added.
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The Covid-19 Riddle: Why Does the Virus Wallop Some Places and Spare Others? - The New York Times
