Category: Covid-19

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Teaneck man indicted over $150 million in false COVID-19 tax credit claims – NorthJersey.com

April 4, 2024

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Researchers predict real-world SARS-CoV-2 evolution by monitoring mutations of viral isolates – Medical Xpress

April 4, 2024

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Junior Associate Professor Kazuo Takayama (Department of Cell Growth and Differentiation) and a collaborative team of researchers in Japan recently characterized SARS-CoV-2 collected from a persistent COVID-19 patient to identify critical factors responsible for generating new mutant strains.

The COVID-19 pandemic, caused by the SARS-CoV-2, was in part due to rapid viral evolution that continuously generated new variants. While multiple factors are known to contribute to the rapid emergence of mutant strains, the principal contributing factors remain unclear.

In this study, accepted for publication in iScience, the researchers collected serum samples and viral isolates from an immunocompromised patient who was persistently infected by the SARS-CoV-2 omicron variant BF.5 over several months to track its evolution and potentially identify crucial factors responsible for the widespread effects of this respiratory virus.

Due to the patient's chronic immunosuppression as part of the treatment for eosinophilic fasciitis, the researchers detected only low antibody levels against SARS-CoV-2 in the samples. Conversely, although they detected cytolytic granules in the sera collected from the patient, suggesting that NK (natural killer) and CD8+ T cells were likely functional, the research team concluded that these immune cells alone, without sufficient antibody production by B cells, were incapable of successfully eliminating the virus from the patient.

The researchers next analyzed the viral genomes isolated from the swab samples by next-generation sequencing methods. Remarkably, whereas the viral genome mutated at 10 locations between the first and two sampling time points (days 0 and 17), it changed at 27 and 37 locations, respectively, during the second and third intervals between sample collection (days 1856 and 57119), suggesting the virus could acquire mutations quicker when left unchecked for more than two months.

Utilizing their expertise in organoid technology, the researchers examined the viruses' replicative capacity and their effects on host cells by infecting human iPS cell-derived lung organoids with each viral isolate. They detected similar viral gene expression and production levels across isolates, demonstrating that infectivity was generally unaltered during the entire course of a persistent SARS-CoV-2 infection.

In addition, the researchers examined the sensitivity to the antiviral drug, remdesivir, and anti-spike protein antibody therapy, sotrovimab, using lung organoids. All viral isolates showed high susceptibility to remdesivir treatment but were largely resistant to the antibody therapy, as they all contained spike protein mutations (G339D or R346T) known to render them resistant to sotrovimab. These findings indicate that infectivity and sensitivity to antiviral treatments remained mostly unchanged despite acquiring multiple mutations during the persistent infection.

To determine whether studying the viral isolates from a patient suffering from persistent SARS-CoV-2 infection can help predict viral evolution, the researchers focused on the S protein amino acid sequence and performed comparative phylogenetic analysis with emergent SARS-CoV-2 strains arising in the real world.

Notably, several mutations (D574N, S975N, S1003I, and A1174V), relatively rare before the appearance of BF.5, were acquired during persistent infection, indicating that they may be more likely to emerge from the BF.5 strain. The research team thus tested this idea by examining the frequency of these mutations in strains appearing after the BF.5 strain.

Remarkably, the D574N and S1003I mutations were detected in greater than 1% of descendant strains emerging from BA.5 (BA.5.24, BA.5.2.36, CG.1, BF.7.26, and BQ.1.1.21), thus demonstrating the potential and real-world implication of analyzing viral evolution in persistently infected patients.

In summary, the study illustrates the potential of studying how viruses change over time in patients with persistent infections as a parallel to predict how they may evolve in the real world. However, it is crucial to note that additional persistently infected patients must be included to determine the general applicability of such findings because individual differences could potentially skew viral evolution.

More information: Hiroki Futatsusako et al, Longitudinal analysis of genomic mutations in SARS-CoV-2 isolates from persistent COVID-19 patient, iScience (2024). DOI: 10.1016/j.isci.2024.109597

Journal information: iScience

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Researchers predict real-world SARS-CoV-2 evolution by monitoring mutations of viral isolates - Medical Xpress

Anxiety really has increased over the past 10 years but why? – New Scientist

April 4, 2024

OVER the past few years, I have noticed an increasing number of people sharing their experiences of feeling anxious, whether it is celebrities opening up in interviews or friends chatting over a drink. This got me thinking: are more people feeling anxious these days or are they just more willing to talk about it?

This apparent uptick seems to be seen in studies of anxiety prevalence but dig into the details and the picture isnt so clear. As for what is behind this possible rise, the covid-19 pandemic is an obvious cause, yet it isnt the only one: economic and political factors may also play a role.

Lets look at the pandemic first. It was a phenomenon that none of us had experienced, a global issue that understandably caused a huge amount of stress, says David Smithson at the charity Anxiety UK. Who wouldnt be worried?

Levels of anxiety rose at the start of the pandemic, with the World Health Organization reporting a 25.6 per cent increase in anxiety disorders in 2020 as lockdowns and other restrictions were brought in and people grappled with an unknown virus and its impact on their lives. But this rise didnt persist, according to a review of 177 studies looking at people in high-income countries, with levels falling as the pandemic continued.

This chimes with Smithsons experience. We saw that rise through demand for our support services from the start of the pandemic for about two years, he says. We have seen, in the last 12 months or so, that demand has dipped down

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Anxiety really has increased over the past 10 years but why? - New Scientist

Leading cause of death unchanged, with one pandemic exception – STAT

April 4, 2024

The leading causes of death havent changed since 1990 with one glaring, pandemic-sized exception.

According to the latest analysis of the Global Burden of Disease study, which reviewed deaths from 288 causes in over 200 states and territories, Covid-19 was the only condition that broke into the ranks if only for two years of the global populations traditional top five killers: ischemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2020 and 2021, Covid-19 was the second-leading cause, pushing stroke to third position.

The study, published Thursday in The Lancet, is the most comprehensive effort to quantify health gains and losses around the world. It found that the years 2020 and 2021 undid a lot of earlier success in increasing life expectancy, which had risen by 6.2 years between 1990 and 2019, only to fall by 1.6 years globally during the pandemic (with another 0.6 years attributed to pandemic-related causes). And while the progress continued in some pockets (for instance, in East Sub-Saharan Africa) even during the pandemic, the report also points to persisting inequities.

The regional variations tell the story. In the Andean region of Latin America, the loss in life expectancy was close to 5 years, and in Southern Sub-Saharan Africa, it was 3.4 years. Latin America and the Caribbean, and Sub-Saharan Africa were the only two regions where Covid-19 was the leading cause of death in 2020.

Conversely, high-income countries overall lost about a year of life expectancy attributable to Covid-19, with the high-income Asia Pacific region experiencing virtually no loss of life expectancy. The same was true for East Asia, which the analysis suggests may have been due to successful containment strategies.

Overall, the single main factor of progress between 1990 and 2021 has been reduction in deaths from diarrhea (which added an average 1.1 year globally). The greatest impact from this decline in deaths from diarrhea was in East Sub-Saharan Africa, where it contributed to a gain of nearly 11 years in life expectancy. East Asia, which with 8.3 years had the second-largest gain in life expectancy, saw dramatic reduction in chronic obstructive pulmonary disease, which contributed an overall 0.9 to global life expectancy.

Each of the regions studied by the report showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic, write the GBD 2021 Causes of Death Collaborators, who comprise hundreds of researchers led by Mohsen Naghavi and Kanyin Liane Ong of the Institute for Health Metrics and Evaluation at the University of Washington. Yet the findings also show persistent inequalities, noted Debra Full-Holden, a professor of epidemiology and the dean of New York Universitys School of Global Public Health.

During Covid, we took our eye off the ball on other diseases we lost some progress on HIV, on malaria the impact of that is always felt the strongest in the lower-resourced parts of the world, she said. With all our innovations in health and health care and vaccines and all of these mitigation strategies, were just not doing the work on a global scale equitably. And I think this data shows that.

For instance, she noted that during the pandemic there was life expectancy loss from malaria, with 90% of global cases concentrated in a region of Africa where only 12% of the worlds population lives, according to the report.

This, she said, shows a global failure to put resources where they are needed. Think about the monkeypox outbreak that we just had. The U.S. and Canada have thrown away millions of doses of vaccines that could have prevented M-pox. They just let it literally expire on the shelf, when we have parts of sub-Saharan Africa where the death rate from monkeypox is the same as the death rate from Covid in the U.S., said Full-Holden.

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A similar trend is highlighted by another data point. In 1990, 44 of the main causes of death were highly concentrated in geographic areas with less than half the global population. In 2021, this was the case for 58 causes of death a pattern showing that interventions that are helping improve health conditions globally arent necessarily reaching all of the world equitably.

While the report does highlight disparities, it also illustrates great success over time for a lot of these diseases, said Eve Wool, a senior research manager at the Institute for Health Metrics and Evaluation and a senior author of the paper. We hope that the paper can be used like a roadmap for people to be able to look at places that have successful disease mitigation programs, like these drastic reductions in enteric infections, and be able to learn from those lessons for the places that are still experiencing those disparities, she said.

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Leading cause of death unchanged, with one pandemic exception - STAT

COVID-sniffing dogs deployed in California – NewsNation Now

April 4, 2024

(Image courtesy Alameda Health System)

(KRON) COVID-19-virus-detecting dogs are being deployed into some California health centers.

Scarlett, a three-year-old yellow labrador and medical detection dog, along with her canine coworker Rizzo, were working their tails off and making friends recently at Park Bridge Rehabilitation & Wellness, Alameda Health System officials wrote.

The dynamic dog duo is part ofa pilot program for identifying cases within the countys health centers. The adorable dogs received warm reactions from clients and staff members who were sniffed at Park Bridge Rehabilitation & Wellness.

We were excited to partner with Early Alert Canines (EAC) for this pilot and our residents and staff loved the opportunity to pet and socialize with Scarlett and Rizzo after the testing, said Luzviminda Lukban, an associate administrator at the rehab.

The yellow labs are trained to sniff near an persons feet, ankles, or lower leg. If they locate the COVID scent, Scarlett and Rizzo will alert their handler by quickly sitting down.

Health officials said the yellow labs noses can detect volatile organic compounds associated with COVID-19. Scarlett and Rizzo have undergone rigorous training with an average accuracy detection rate of 94 percent, health officials said. The dogs can sniff-test approximately 300 people in 30 minutes.

Park Bridge residents and staff members were pre-screened to ensure they were not allergic to or afraid of dogs. Scarlett and Rizzo are accompanied by humans who administer COVID-19 antigen tests if needed.

In 2021 EAC partnered with the California Department of Public Health in a program to train and deploy COVID detection dogs in Bay Area schools. The next phase will focus on vulnerable populations in skilled nursing facilities.

The cost to train each dog is about $50,000 and can take up to a year.

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COVID-sniffing dogs deployed in California - NewsNation Now

COVID-19-Associated Bilateral Avascular Necrosis of Femoral Head in a Young Male Without Corticosteroid Exposure … – Cureus

April 4, 2024

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A rigorous theoretical and numerical analysis of a nonlinear reaction-diffusion epidemic model pertaining dynamics of … – Nature.com

April 4, 2024

This section presents qualitative analysis of the reaction-diffusion COVID-19 compartmental epidemic model (1). We proceed to prove the basic mathematical properties of the model solution as follows.

One of the most important properties of an epidemic model is the solution boundedness. We take into consideration the approach described in28 in order to analyze the solution boundedness of the problem (1). The result is given in the following Theorem.

The solution of the model (1) i.e., ((S(.,tilde{t}), E(.,tilde{t}), I_1(.,tilde{t}), I_2(.,tilde{t}), I_3(.,tilde{t}), R(.,tilde{t}))) is bounded (forall) (tilde{t}ge 0).

In order to prove the desired result, add all equations in the model (1)

$$begin{aligned}&dfrac{partial }{partial tilde{t}}S(tilde{t},tilde{y})+dfrac{partial }{partial tilde{t}}E(tilde{t},tilde{y})+dfrac{partial }{partial tilde{t}}I_1(tilde{t},tilde{y})+dfrac{partial }{partial tilde{t}}I_2(tilde{t},tilde{y})+dfrac{partial }{partial tilde{t}}I_3(tilde{t},tilde{y})+dfrac{partial }{partial tilde{t}}R(tilde{t},tilde{y}), \&= D_1dfrac{partial ^2}{partial tilde{y}^2}S(tilde{t},tilde{y})+D_2dfrac{partial ^2}{partial tilde{y}^2}E(tilde{y}, t)+D_3dfrac{partial ^2}{partial tilde{y}^2}I_1(tilde{t},tilde{y})+D_4dfrac{partial ^2}{partial tilde{y}^2}I_2(tilde{t},tilde{y})+D_5dfrac{partial ^2}{partial tilde{y}^2}I_3(tilde{t},tilde{y})\&quad +D_6dfrac{partial ^2}{partial tilde{y}^2}R(tilde{t},tilde{y})+Pi -zeta (S(tilde{t},tilde{y})+E(tilde{t},tilde{y})+I_1(tilde{t},tilde{y})+I_2(tilde{t},tilde{y})+I_3(tilde{t},tilde{y})+R(tilde{t},tilde{y}))\&quad -xi _{1}I_1(tilde{t},tilde{y})-xi _{2}I_2(tilde{t},tilde{y}). end{aligned}$$

Integrating over (Lambda), using the well known Divergence theorem29 and make use of no flux boundary conditions, which yield to

$$begin{aligned}&int _{Lambda }bigg {dfrac{partial }{partial tilde{t}}S(tilde{t},tilde{y})+dfrac{partial }{partial tilde{t}}E(tilde{t},tilde{y})+dfrac{partial }{partial tilde{t}}I_1(tilde{t},tilde{y})+dfrac{partial }{partial tilde{t}}I_2(tilde{t},tilde{y})+dfrac{partial }{partial tilde{t}}I_3(tilde{t},tilde{y})+dfrac{partial }{partial tilde{t}}R(tilde{t},tilde{y}) bigg }dtilde{y},\&=Pi |Lambda |-dint _{Lambda }bigg {(S(tilde{t},tilde{y})+E(tilde{t},tilde{y})+I_1(tilde{t},tilde{y})+I_2(tilde{t},tilde{y})+I_3(tilde{t},tilde{y})+R(tilde{t},tilde{y}))bigg }dtilde{y}\&quad -int _{Lambda }bigg {xi _{1}I_1(tilde{t},tilde{y})+xi _{2}I_2(tilde{t},tilde{y})bigg }dtilde{y},\&le Pi |Lambda |-zeta N(tilde{t}).\ dfrac{d}{dtilde{t}}N(tilde{t})&=Pi |Lambda |-zeta N(tilde{t}). end{aligned}$$

It gives

$$begin{aligned}0le N(tilde{t})le dfrac{Pi |Lambda |}{zeta }-exp (-zeta tilde{t}) N(0), quad ;forall ; tilde{t}ge 0.end{aligned}$$

Hence

$$begin{aligned} lim limits _{trightarrow +infty } N(t)le dfrac{Pi |Lambda |}{zeta }. end{aligned}$$

(square)

A positively invariant set for the system (1) is defined as follows:

$$begin{aligned} Phi = bigg {(S(tilde{t},tilde{y}), E(tilde{t},tilde{y}), I_1(tilde{t},tilde{y}), I_2(tilde{t},tilde{y}), I_3(tilde{t},tilde{y}), R(tilde{t},tilde{y})^{T}in mathbb {R}_+^6: N(tilde{t})le dfrac{Pi |Lambda |}{d}bigg }subset mathbb {R}_+^6. end{aligned}$$

For derivation of the threshold parameter known as the basic reproductive number, we used the well-known approached considered in30. Model (1) possess two equilibria i.e. the disease-free equilibrium (DFE) and the endemic equilibrium (EE) represented by (Xi _0) and (Gamma _{EE}) respectively such that:

$$begin{aligned} Gamma _0 = (S_0, E_0, I_{1_0}, I_{2_0}, I_{3_0}, R_0)= left( Pi /zeta , 0, 0, 0, 0, 0right) . end{aligned}$$

The EE is calculated as follows:

$$begin{aligned} Gamma _{EE} = (S^*, E^*, {I^*_{1}}, {I^*_{2}}, {I^*_{3}}, R^*), end{aligned}$$

with the following analytical values

$$begin{aligned} {left{ begin{array}{ll} \ S^{*} = dfrac{Pi text{c{N}}}{text{c{N}}zeta +mathfrak {R}^0-1}, \ \ E^{*} = dfrac{Pi (mathfrak {R}^0-1)}{alpha _1left( text{c{N}}zeta +mathfrak {R}^0-1 right) },\ \ I_1^{*} = dfrac{Pi mathfrak {R}_{01}(mathfrak {R}^0-1)}{beta left( text{c{N}}zeta +mathfrak {R}^0-1 right) },\ \ I_2^{*} = dfrac{Pi mathfrak {R}_{01}(mathfrak {R}^0-1)}{beta _Pleft( text{c{N}}zeta +mathfrak {R}^0-1 right) },\ \ I_3^{*} = dfrac{Pi mathfrak {R}_{03}(mathfrak {R}^0-1)}{beta psi left( text{c{N}}zeta +mathfrak {R}^0-1right) }, \ \ R^* = dfrac{Pi }{zeta }left[ eta _1dfrac{mathfrak {R}_{01}}{beta }+eta _2dfrac{mathfrak {R}_{02}}{beta _P}+eta _3dfrac{mathfrak {R}_{03}}{beta psi } right] dfrac{(mathfrak {R}^0-1)}{left( text{c{N}}zeta +mathfrak {R}^0-1right) }, end{array}right. } end{aligned}$$

and

$$begin{aligned} text{c{N}} = frac{1}{r+zeta }+left( 1+frac{eta _1}{zeta }right) frac{mathfrak {R}_{01}}{beta }+left( 1+frac{eta _2}{zeta }right) frac{mathfrak {R}_{01}}{beta _P}+left( 1+frac{eta _3}{zeta }right) frac{mathfrak {R}_{03}}{beta }. end{aligned}$$

Moreover, the basic reproductive number (mathfrak {R}^0) is computed as follows:

The infectious classes in the proposed model (1) are (E, I_1, I_2) and (I_3), Henceforth, the vectors below present the transmission of newborn infections and the transitions between various classes.

$$begin{aligned} {textbf {F}}= left( begin{array}{c} beta frac{(I_1+psi I_3)S}{N}+beta _Pfrac{ I_2 S}{N}\ 0\ 0\ 0\ end{array}right) , quad quad {textbf {V}} = left( begin{array}{c} (r+zeta )E\ -r k_1 E+(eta _1+zeta +zeta _{1})I_1\ -r k_2 E+(eta _2+zeta +zeta _{2})I_2\ -r(1-k_1-k_2) E+(eta _3+zeta )I_3 end{array} right) , end{aligned}$$

The Jacobian of above matrices are evaluated as:

$$begin{aligned} mathcal {F}= & {} left( begin{array}{cccc} 0 &{} beta &{} beta _mathcal {P} &{} psi beta \ 0 &{} 0 &{} 0 &{} 0\ 0 &{} 0 &{} 0 &{} 0\ 0 &{} 0 &{} 0 &{} 0\ end{array}right) ,\ mathcal {V}= & {} left( begin{array}{cccc} r+zeta &{} 0 &{} 0 &{} 0\ -r k_1 &{} eta _1+zeta _1+zeta &{} 0 &{} 0\ -r k_1 &{} 0 &{} eta _2+zeta _2+zeta &{} 0\ -r(1-k_1-k_2) &{} 0 &{} 0 &{} eta _3+zeta \ end{array}right) , end{aligned}$$

and (N = dfrac{Pi }{zeta }) in case of disease-free equilibrium. Thus, the associated next generation matrix is,

$$begin{aligned} mathcal {F}mathcal {V}^{-1} = left( begin{array}{cccc} frac{rbeta psi (1-k_1-k_2)}{(r+zeta )(eta _3+zeta )}+frac{rbeta k_1 }{(r+zeta )(eta _1+zeta _{1}+zeta )}+frac{rbeta _P k_2 }{(r+zeta )(eta _2+zeta _{2}+zeta )} &{} frac{beta }{(eta _1+zeta _{1}+zeta )} &{} frac{beta _P }{(eta _2+zeta _{2}+zeta )} &{} frac{beta psi }{(eta _3+zeta )}\ 0 &{} 0 &{} 0 &{} 0\ 0 &{} 0 &{} 0 &{} 0\ 0 &{} 0 &{} 0 &{} 0 end{array} right) . end{aligned}$$

The basic reproductive number which is the spectral radius of (FV^{-1}) and is given by:

$$begin{aligned} mathfrak {R}^0 = rho left( FV^{-1}right) = mathfrak {R}_{01}+mathfrak {R}_{02}+mathfrak {R}_{03}, end{aligned}$$

(5)

where

$$begin{aligned} mathfrak {R}_{01} =frac{rbeta k_1 }{(r+zeta )(eta _1+zeta +zeta _{1})}, mathfrak {R}_{02}= frac{rbeta _P k_2 }{(r+zeta )(eta _2+zeta +zeta _{2})} text {and} mathfrak {R}_{03}= frac{rbeta psi (1-k_1-k_2)}{(r+zeta )(eta _3+zeta )}. end{aligned}$$

The DFE (Gamma _0) is stable locally asymptomatically in (Phi) if (mathfrak {R}^0<1), otherwise it is unstable.

The Jacobian of (1) at the DFE point (Gamma _0) is

$$begin{aligned} J(Gamma _{0}) = left( begin{array}{cccccc} -zeta &{} 0 &{} -beta &{} -beta _P &{} -beta psi &{} 0\ 0 &{} -r-zeta &{} beta &{} beta _P &{} beta psi &{} 0\ 0 &{} r k_1 &{} -eta _{1}-zeta -zeta _1 &{} 0 &{} 0 &{} 0 \ 0 &{} r k_2 &{} 0 &{} -eta _{2}-zeta -zeta _2 &{} 0 &{} 0 \ 0 &{} r(1-k_{1}-k_{2}) &{} 0 &{} 0 &{} -zeta -eta _3 &{} 0 \ 0 &{} 0 &{} eta _1 &{} eta _2 &{} eta _3 &{} -zeta end{array}right) , end{aligned}$$

and the characteristic polynomial associated to above matrix is given as follows:

$$begin{aligned} P(varphi ) = (zeta +varphi )^2(varphi ^{3}+c_{2}varphi ^{2}+c_{1}varphi +c_{0}), end{aligned}$$

(6)

where,

$$begin{aligned} c_{2}&= b_1l_2(1-mathfrak {R}_{01})+l_1l_3(1-mathfrak {R}_{02})+l_1l_4(1-mathfrak {R}_{03})+l_2l_3+l_2l_4+l_3l_4,\ c_{1}&= l_1l_2(l_3+l_4)(1-mathfrak {R}_{01})+l_1l_3l_4(1-mathfrak {R}_{03}-mathfrak {R}_{02})+l_1l_4(l_3-l_2)mathfrak {R}_{03}\&quad -l_1l_2l_3mathfrak {R}_{02}, \ c_{0}&=(r+zeta )(eta _3+zeta )(eta _1+zeta _1+zeta )(eta _2+zeta _2+zeta ) left( 1-mathfrak {R}^0 right) >0 text {for} mathfrak {R}^0<1, end{aligned}$$

The values of (c_0, c_1, c_2) in (6) are claimed to be positive under the condition (mathfrak {R}^0<1). Further, (c_1c_2-c_3>0) confirming the Routh-Hurwitz conditions. Hence, the (texttt {DFE}) stable locally when (mathfrak {R}^0<1). (square)

To discuss the local stability of endemic equilibria (Gamma _{EE}), the model (1) is linearized at (Gamma _{EE} = (S^*, E^*, I^*_{1}, I^*_{2}, I^*_{3}, R^* )). For this purpose assume that

$$begin{aligned} left. begin{array}{ll} \ S(tilde{t},tilde{y}) &{}= bar{S}(tilde{t},tilde{y})+S^*,\ \ E(tilde{t},tilde{y}) &{}= bar{E}(tilde{t},tilde{y})+E^*,\ \ I_1(tilde{t},tilde{y}) &{}= bar{I}_{1}(tilde{t},tilde{y})+I^*_{1},\ \ I_2(tilde{t},tilde{y}) &{}= bar{I}_{2}(tilde{t},tilde{y})+I^*_{2},\ \ I_3(tilde{t},tilde{y}) &{}= bar{I}_{3}(tilde{t},tilde{y})+I^*_{3},\ \ R(tilde{t},tilde{y}) &{}= bar{R}(tilde{t},tilde{y})+R^*. end{array} right} end{aligned}$$

(7)

(bar{S}(tilde{t},tilde{y}), bar{E}(tilde{t},tilde{y}), bar{I}_{1}(tilde{t},tilde{y}), bar{I}_{2}(tilde{t},tilde{y}), bar{I}_{3}(tilde{t},tilde{y})) and (bar{R}(tilde{t},tilde{y})) are minimal perturbation. The linearized form of the problem (1) is given by (8),

$$begin{aligned} left. begin{array}{ll} \ dfrac{partial bar{S}}{partial tilde{t}} &{}= D_1dfrac{partial ^2 bar{S}}{partial tilde{y}^2}+g_{11}bar{S}+g_{12}bar{E}+g_{13}bar{I}_{1}+g_{14}bar{I}_2+g_{15}bar{I}_3+g_{16}bar{R},\ \ dfrac{partial bar{E}}{partial tilde{t}} &{}= D_2dfrac{partial ^2 bar{E}}{partial tilde{y}^2}+g_{21}bar{S}+g_{22}bar{E}+g_{23}bar{I}_{1}+g_{24}bar{I}_2+g_{25}bar{I}_3+g_{26}bar{R},\ \ dfrac{partial bar{I}_1}{partial tilde{t}} &{}= D_3dfrac{partial ^2 bar{I}_1}{partial tilde{y}^2}+g_{31}bar{S}+g_{32}bar{E}+g_{33}bar{I}_{1}+g_{34}bar{I}_2+g_{35}bar{I}_3+g_{36}bar{R},\ \ dfrac{partial bar{I}_2}{partial tilde{t}} &{}= D_4dfrac{partial ^2 bar{I}_2}{partial tilde{y}^2}+g_{41}bar{S}+g_{42}bar{E}+g_{43}bar{I}_{1}+g_{44}bar{I}_2+g_{45}bar{I}_3+g_{46}bar{R},\ \ dfrac{partial bar{I}_3}{partial tilde{t}} &{}= D_5dfrac{partial ^2 bar{I}_3}{partial tilde{y}^2}+g_{51}bar{S}+g_{52}bar{E}+g_{53}bar{I}_{1}+g_{54}bar{I}_2+g_{55}bar{I}_3+g_{56}bar{R},\ \ dfrac{partial bar{R}}{partial tilde{t}} &{}= D_6dfrac{partial ^2 bar{R}}{partial tilde{y}^2}+g_{61}bar{S}+g_{62}bar{E}+g_{63}bar{I}_{1}+g_{64}bar{I}_2+g_{65}bar{I}_3+g_{66}bar{R}, end{array} right} end{aligned}$$

(8)

such that

$$begin{aligned} begin{array}{ll} &{}g_{11} = -dfrac{beta }{N}left( I^*_{1}+psi I^*_{3}right) -dfrac{beta _P}{N}I^*_{2}-zeta , g_{12} = 0, g_{13} = -dfrac{beta }{N}S^*, g_{14} = -dfrac{beta _P}{N}S^*, \ &{}g_{15} = -dfrac{beta psi _{1}}{N}S^*,hspace{0.3cm} g_{16} = 0,hspace{0.3cm} g_{21} = dfrac{beta }{N}left( I^*_{1}+psi I^*_{3}right) +dfrac{beta _P}{N}I^*_{2}, hspace{0.3cm} g_{22} = -(r+zeta ), \ &{} g_{23} = dfrac{beta }{N}S^*, hspace{0.3cm} g_{24} = dfrac{beta _P}{N}S^*, hspace{0.3cm} g_{25} = dfrac{beta psi }{N}S^*, hspace{0.3cm} g_{26} = 0, hspace{0.3cm} g_{31} = 0, hspace{0.3cm} g_{32} = r k_1, \ &{}g_{33} = -(eta _{1}+zeta +zeta _1), hspace{0.3cm} g_{34} = 0, hspace{0.3cm} g_{35} = 0, hspace{0.3cm} g_{36} = 0, hspace{0.3cm} g_{41} = 0, hspace{0.3cm} g_{42} = r k_2,\ &{}g_{43} = 0, hspace{0.3cm} g_{44} = -(eta _{2}+zeta +zeta _2), hspace{0.3cm} g_{45}=0, hspace{0.3cm} g_{46}=0, hspace{0.3cm} g_{51} = 0, hspace{0.3cm} g_{56} = 0,\ &{}g_{52} = r(1-k_1-k_2), hspace{0.3cm} g_{53} = 0, g_{54} = 0, g_{55} = -(eta _{3}+zeta ), hspace{0.3cm}g_{61} = 0,\ &{}g_{62} = 0, hspace{0.3cm} g_{55} = -(eta _{3}+zeta ), hspace{0.3cm} g_{63} = eta _1, hspace{0.3cm} g_{64}=eta _2, hspace{0.3cm} g_{65}=eta _3, hspace{0.3cm} g_{66} = -zeta . end{array} end{aligned}$$

Given that the linearized system (8) has a solution in Fourier series form, then

$$begin{aligned} left. begin{array}{ll} \ bar{S}(tilde{t},tilde{y}) &{}= sum limits _{k}e^{lambda t}b_{1k}cos (ktilde{y}), \ \ bar{E}(tilde{t},tilde{y}) &{}= sum limits _{k}e^{lambda t}b_{2k}cos (ktilde{y}), \ \ bar{I}_{1}(tilde{t},tilde{y}) &{}= sum limits _{k}e^{lambda t}b_{3k}cos (ktilde{y}), \ \ bar{I}_{2}(tilde{t},tilde{y}) &{}= sum limits _{k}e^{lambda t}b_{4k}cos (ktilde{y}), \ \ bar{I}_{3}(tilde{t},tilde{y}) &{}= sum limits _{k}e^{lambda t}b_{5k}cos (ktilde{y}), \ \ bar{R}(tilde{t},tilde{y}) &{}= sum limits _{k}e^{lambda t}b_{6k}cos (ktilde{y}). end{array} right} end{aligned}$$

(9)

In above, (k =frac{npi }{2}), with (nin Z^{+}), indicates wave-number for the node n. Using (9) in (8) yield to,

$$begin{aligned} left. begin{array}{ll} \ sum limits _{k}(g_{11}-k^2D_1-lambda )b_{1k}+sum limits _{k}g_{12}b_{2k} +sum limits _{k}g_{13}b_{3k}+sum limits _{k}g_{14}b_{4k}+sum limits _{k}g_{15}b_{5k}+sum limits _{k}g_{16}b_{6k}&{}=0, \ \ sum limits _{k}g_{21}b_{1k}+sum limits _{k}(g_{22}-k^2D_{2}-lambda )b_{2k}+sum limits _{k}g_{23}b_{3k}+sum limits _{k}g_{24}b_{4k}+sum limits _{k}g_{25}b_{5k}+sum limits _{k}g_{26}b_{6k}&{}=0, \ \ sum limits _{k}g_{31}b_{1k}+sum limits _{k}g_{32}b_{2k}+sum limits _{k}(g_{33}-k^2D_{I_1}-lambda )b_{3k}+sum limits _{k}g_{34}b_{4k}+sum limits _{k}g_{35}b_{5k}+sum limits _{k}g_{36}b_{6k} &{}=0, \ \ sum limits _{k}g_{41}b_{1k}+sum limits _{k}g_{42}b_{2k}+sum limits _{k}g_{43}b_{3k}+sum limits _{k}(g_{44}-k^2D_4-lambda )b_{4k}+sum limits _{k}g_{45}b_{5k}+sum limits _{k}g_{46}b_{6k} &{}=0, \ \ sum limits _{k}g_{51}b_{1k}+sum limits _{k}g_{52}b_{2k}+sum limits _{k}g_{53}b_{3k}+sum limits _{k}g_{54}b_{4k}+sum limits _{k}(g_{44}-k^2D_5-lambda )b_{5k}+sum limits _{k}g_{56}b_{6k} &{}=0, \ \ sum limits _{k}g_{61}b_{1k}+sum limits _{k}g_{62}b_{2k}+sum limits _{k}g_{63}b_{3k}+sum limits _{k}g_{64}b_{4k}+sum limits _{k}g_{65}b_{5k}+sum limits _{k}(g_{66}-k^2D_{R}-lambda )b_{6k} &{}=0. end{array} right} end{aligned}$$

(10)

The matrix V representing the variational matrix of (8) is

$$begin{aligned} V = left( begin{array}{cccccc} -c_{11} &{} 0 &{} -g_{13}&{} -g_{14}&{} -g_{15}&{} 0\ g_{21} &{} -c_{22}&{} g_{23} &{} g_{24}&{} g_{25}&{} 0\ 0 &{}g_{23}&{}-c_{33}&{} 0&{} 0&{} 0\ 0 &{}g_{42}&{}0&{}-c_{44}&{} 0&{} 0\ 0 &{}g_{52}&{}0&{} 0&{}-c_{55}&{} 0\ 0 &{}0&{}g_{63}&{} g_{64}&{} g_{65}&{}-c_{66}\ end{array}right) , end{aligned}$$

(11)

where,

$$begin{aligned} c_{11}&= k^2D_1+g_{11}, \ c_{22}&= k^2D_{2}+g_{22}, \ c_{33}&= k^2D_3+g_{33}, \ c_{44}&= k^2D_4+g_{44},\ c_{55}&= k^2D_5+g_{55}, \ c_{66}&= k^2D_{R}+g_{66}. end{aligned}$$

The subsequent polynomial is,

$$begin{aligned} P(lambda ) = (lambda +c_{66})(lambda ^{5}+mathcal {A}_{4}lambda ^{4}+mathcal {A}_{3}lambda ^{3}+mathcal {A}_{2}lambda ^{2}+mathcal {A}_{1}lambda +mathcal {A}_{0}). end{aligned}$$

(12)

The relative coefficient values are;

$$begin{aligned} mathcal {A}_{4}&= c_{11}+k^{2}(D_2+D_3+D_4+D_5+D_6)+g_{11}+g_{22}+g_{33}+g_{44},\ mathcal {A}_{3}&= b_1+g_{22}left( g_{33}left( 1-dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}right) +g_{44}left( 1-dfrac{mathfrak {R}_{02}}{mathfrak {R}^0}right) +g_{55}left( 1-dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}right) right) ,\ mathcal {A}_{2}&=b_2+b_3+c_{11}g_{22}left( g_{33}left( 1-dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}right) +g_{44}left( 1-dfrac{mathfrak {R}_{02}}{mathfrak {R}^0}right) +g_{55}left( 1-dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}right) right) +g_{22}\&quad times g_{33}(D_5+D_4)k^{2}left( 1-dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}right) +g_{22}g_{44}(D_5+D_3)k^{2}left( 1-dfrac{mathfrak {R}_{02}}{mathfrak {R}^0}right) +g_{22}g_{55}k^{2}\&quad times (D_3+D_4)left( 1-dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}right) +g_{22}g_{33}g_{44}left( 1-dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}-dfrac{mathfrak {R}_{02}}{mathfrak {R}^0} right) +g_{22}g_{33}g_{55}\&quad times left( 1-dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}-dfrac{mathfrak {R}_{02}}{mathfrak {R}^0} right) +g_{22}g_{44}g_{55}left( 1-dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}-dfrac{mathfrak {R}_{02}}{mathfrak {R}^0}right) +g_{21}g_{22}g_{33}dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}+ g_{21}g_{22}\&quad times g_{55}dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}+g_{21}g_{22}g_{44}dfrac{mathfrak {R}_{02}}{mathfrak {R}^0}, \ mathcal {A}_{1}&= b_4+g_{22}g_{33}D_5D_4k^{4}left( 1-dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}right) +c_{11}left( g_{22}g_{33}(D_5+D_4)k^{2}left( 1-dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}right) right) \&quad +g_{22}g_{44}D_5D_3k^{4}left( 1-dfrac{mathfrak {R}_{02}}{mathfrak {R}^0}right) +c_{11}left( g_{22}g_{44}(D_5+D_3)k^{2}left( 1-dfrac{mathfrak {R}_{02}}{mathfrak {R}^0}right) right) +c_{11}\&quad times left( g_{22}g_{55}(D_4+D_3)k^{2}left( 1-dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}right) right) +c_{11}g_{22}g_{33}g_{44}left( 1-dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}-dfrac{mathfrak {R}_{02}}{mathfrak {R}^0}right) +c_{11} \&quad times g_{22}g_{55}left( g_{33}left( 1-dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}-dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}right) +g_{44}left( 1-dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}-dfrac{mathfrak {R}_{02}}{mathfrak {R}^0}right) right) +g_{22}g_{33}g_{44}D_5k^{2}\&quad times left( 1-dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}-dfrac{mathfrak {R}_{02}}{mathfrak {R}^0}right) +g_{22}g_{55}D_4D_3k^{4}left( 1-dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}right) +g_{22}g_{33}g_{55}D_4k^{2}\&quad times left( 1-dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}-dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}right) +g_{22}g_{44}g_{55}D_3k^{2}left( 1-dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}-dfrac{mathfrak {R}_{02}}{mathfrak {R}^0}right) +g_{21}(c_{44}+c_{55})g_{22}\&quad times g_{33}dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}+g_{21}(c_{33}+c_{44})g_{22}g_{55}dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}+g_{21}(c_{33}+c_{55})g_{22}g_{44}dfrac{mathfrak {R}_{02}}{mathfrak {R}^0},\ mathcal {A}_{0}&= b_5 +c_{11}g_{22}g_{44}D_5D_3k^{4}left( 1-dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}right) +c_{11}g_{22} g_{55}D_4D_3k^{4}left( 1-dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}right) +c_{11}g_{22}\&quad times g_{33}g_{44}D_5k^{2}left( 1-dfrac{mathfrak {R}_{02}}{mathfrak {R}^0}right) +c_{11}g_{22}g_{33}g_{55}D_4k^{2}left( 1-dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}right) +c_{11}g_{22}g_{44}g_{55}D_3k^{2}\&quad times left( 1-dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}right) +c_{11}g_{22}g_{33}g_{44}g_{55}left( 1-dfrac{mathfrak {R}_{03}}{mathfrak {R}^0}-dfrac{mathfrak {R}_{02}}{mathfrak {R}^0}right) -c_{11}g_{22}g_{33}D_5D_4k^{4}dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}\&quad -c_{11}g_{22}g_{33}g_{44}k^{2}left( D_4dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}+D_3dfrac{mathfrak {R}_{02}}{mathfrak {R}^0}right) -c_{11}c_{55}g_{22}g_{44}^{2}dfrac{mathfrak {R}_{01}}{mathfrak {R}^0}. end{aligned}$$

The values of (b_i), where (i=1,ldots ,5) are given below:

$$begin{aligned} b_1&= c_{11} (c_{22}+c_{33}+c_{44}+c_{55})+g_{33}left( D_2k^{2}+c_{44}+c_{55}right) +g_{44}k^{2}left( D_2+D_3+D_5right) \&quad +g_{55}left( (D_2+D_3)k^{2}+c_{44}right) +k^{4}left( D_5(D_4+D_{I_E})+(D_5+D_4)(D_3+D_{I_E})right) \&quad +g_{22}k^{2}left( D_3+D_4+D_5right) ,\ b_2&=c_{11}left( k^{4}left( D_5(D_4+D_{I_E})+(D_5+D_4)(D_3+D_{I_E})right) +g_{44}k^{2}left( D_2+D_5+D_3right) right) \&quad +c_{11}g_{22}k^{2}left( D_3+D_4+D_5right) ,\ b_3&=c_{11}left( g_{44}((D_2+D_3)k^{2}+c_{44})+g_{33}(D_2k^{2}+c_{44}+c_{55})right) +(D_4D_5(D_2+D_3))k^{6}\&quad +(D_2D_3(D_5+D_4))k^{6}+g_{22}(D_4D_4+D_5(D_4+D_3))k^{2}+g_{33}g_{44}k^{2}(D_2+D_5)\&quad +g_{33}(D_5D_4+D_{I_E}(D_5+D_4))k^{2}+g_{44}k^{4}(D_5D_3+D_{I_E}(D_5+D_3))\&quad +g_{55}k^{4}(D_2D_4+D_3(D_2+D_4))+g_{44}g_{55}k^{2}(D_2+D_3)+g_{33}g_{55}(D_2k^{2}+c_{44}),\ b_4&=c_{11}left( D_5D_4(D_2+D_3)+D_2D_3(D_5+D_4)k^{6}+left( (g_{22}+g_{55})D_4D_3right) k^{4} right) \&quad +c_{11}left( (D_4+D_3)(g_{22}D_5+g_{55}D_2)k^{4}+g_{44}left( D_5D_3+D_{I_E}(D_5+D_3)right) k^{4} right) \&quad +c_{11}left( g_{44}left( D_5D_4+D_{I_E}(D_5+D_4)right) k^{4}+g_{55}(g_{33}(D_2+D_4)+g_{44}(D_2+D_5))k^{2} right) \&quad +c_{11}left( g_{44}g_{55}+2(D_2+D_3)k^{2}right) +g_{22}D_3D_5D_4k^{6}+c_{33}c_{44}c_{55}D_2k^{2},\ b_5&=g_{21}c_{22}c_{33}c_{44}c_{55}+c_{11}left( c_{33}k^{2}(g_{22}D_5D_5k^{2})+D_2(c_{44}+c_{55}) right) . end{aligned}$$

The coefficients (mathcal {A}_{i}), (i = 0,ldots , 4), of P given by (13) are positive, if (mathfrak {R}^0>1). Further, it also satisfies the Routh-Hurwitz stability conditions for a polynomial having degree five, i.e.,

$$begin{aligned}&mathcal {A}_{4}mathcal {A}_{3}mathcal {A}_{2}-mathcal {A}_{2}^{2}-mathcal {A}_{4}^{2}mathcal {A}_{1}>0,\&left( mathcal {A}_{4}mathcal {A}_{1}-mathcal {A}_{0}right) left( mathcal {A}_{4}mathcal {A}_{3}mathcal {A}_{2}-mathcal {A}_{2}^2-mathcal {A}_{4}^2mathcal {A}_{1}right) -mathcal {A}_{0}(mathcal {A}_{4}mathcal {A}_{3}mathcal {A}_{2})^2-mathcal {A}_{4}mathcal {A}_{0}^2>0. end{aligned}$$

Therefore, it is asserted that (Gamma _{EE}) is stable locally for (mathfrak {R}^{0}>1).

In the subsequent section, we will investigate the stability of the problem (1) in global case at the steady state (Gamma _{0}) using a nonlinear Lyapunov stability approach.

If (mathfrak {R}^0<1) then the DFE (Gamma _{0}) of the system (1) is globally stable in (Phi).

The Lyapunov-type function is define as

$$begin{aligned} V(tilde{t}) = int _{Lambda }biggl {E(tilde{t},tilde{y})+j_1I_1(tilde{t},tilde{y})+j_2I_2(tilde{t},tilde{y})+j_3I_3(tilde{t},tilde{y})biggr }dtilde{y}, end{aligned}$$

with

$$begin{aligned} j_1 = dfrac{beta }{eta _{1}+zeta +zeta _1}, j_2 = dfrac{beta }{eta _{2}+zeta +zeta _2}, hbox {and} j_3 = dfrac{psi beta }{eta _{1}+zeta }. end{aligned}$$

Differentiating (V(tilde{t},tilde{y})) with the solution of (1) as follows:

$$begin{aligned} dfrac{dV}{dtilde{t}}&= int _{Lambda }biggl {D_{2}dfrac{partial ^2 E(tilde{t},tilde{y})}{partial tilde{y}^2}+j_1D_{3}dfrac{partial ^2 I_1(tilde{t},tilde{y})}{partial tilde{y}^2}+j_2D_{4}dfrac{partial ^2 I_2(tilde{t},tilde{y})}{partial tilde{y}^2}+j_3D_{5}dfrac{partial ^2 I_2(tilde{t},tilde{y})}{partial tilde{y}^2}biggr }dx\&quad +int _{Lambda }biggl {j_1r k_1+j_2r k_2+j_3r(1-k_1-k_2)-(r+zeta ) biggr }E(tilde{t},tilde{y})dtilde{y}\&quad +int _{Lambda }biggl {beta frac{I_1(tilde{t},tilde{y})S(tilde{t},tilde{y})}{N(tilde{t},tilde{y})}+beta psi frac{I_3(tilde{t},tilde{y})S(tilde{t},tilde{y})}{N(tilde{t},tilde{y})}+beta _Pfrac{I_2(tilde{t},tilde{y})S(tilde{t},tilde{y})}{N(tilde{t},tilde{y})} biggr }dtilde{y}\&quad -int _{Lambda }biggl {j_1(eta _{1}+zeta +zeta _{1})I_1+j_2(eta _{2}+zeta +zeta _{2})I_2+j_3(eta _{3}+zeta )I_3biggr }dtilde{y}. end{aligned}$$

As, (S(tilde{t},tilde{y})le N(tilde{t},tilde{y})) for (tilde{y}in Lambda) and (tilde{t}ge 0), therefore,

$$begin{aligned} dfrac{dV}{dtilde{t}}&le int _{Lambda }biggl {D_{2}dfrac{partial ^2 E(tilde{t},tilde{y})}{partial tilde{y}^2}+D_{3}dfrac{partial ^2 I_1(tilde{t},tilde{y})}{partial tilde{y}^2}+D_{4}dfrac{partial ^2 I_2(tilde{t},tilde{y})}{partial tilde{y}^2}+D_{5}dfrac{partial ^2 I_2(tilde{t},tilde{y})}{partial tilde{y}^2}biggr }dtilde{y}\&quad +int _{Lambda }biggl {j_1r k_1+j_2r k_2+j_3r(1-k_1-k_2)-(r+zeta ) biggr }E(tilde{t},tilde{y})dtilde{y}\&quad +int _{Lambda }biggl {beta I_1(tilde{t},tilde{y})+beta psi I_3(tilde{t},tilde{y})+beta _PI_2(tilde{t},tilde{y}) biggr }dtilde{y}\&quad -int _{Lambda }biggl {j_1(eta _{1}+zeta +zeta _{1})I_1+j_2(eta _{2}+zeta +zeta _{2})I_2+j_3(eta _{3}+zeta )I_3biggr }dtilde{y}, \ dfrac{dV}{dtilde{t}}&le int _{Lambda }biggl {D_{2}dfrac{partial ^2 E(tilde{t},tilde{y})}{partial tilde{y}^2}+D_{3}dfrac{partial ^2 I_1(tilde{t},tilde{y})}{partial tilde{y}^2}+D_{4}dfrac{partial ^2 I_2(tilde{t},tilde{y})}{partial tilde{y}^2}+D_{5}dfrac{partial ^2 I_2(tilde{t},tilde{y})}{partial tilde{y}^2}biggr }dtilde{y}\&quad +int _{Lambda }biggl {j_1r k_1+j_2r k_2+j_3r(1-k_1-k_2)-(r+zeta ) biggr }E(tilde{t},tilde{y})dtilde{y}\&quad +int _{Lambda }biggl {(beta -j_1(eta _{1}+zeta +zeta _{1}))I_1(tilde{t},tilde{y})+(beta psi -j_2(eta _{2}+zeta +zeta _{2}))I_3(tilde{t},tilde{y})biggr }dtilde{y}\&quad +int _{Lambda }biggl {(beta _P-j_3(eta _{3}+zeta ))I_2(tilde{t},tilde{y})biggr }dtilde{y}. end{aligned}$$

Using, the criterions described in (2), we have

$$begin{aligned} dfrac{dV}{dtilde{t} }&le (r+zeta )int _{Lambda }biggl {j_1dfrac{r k_1}{(r+zeta )}+j_2dfrac{r k_2}{(r+zeta )}+j_3dfrac{r(1-k_1-k_2)}{(r+zeta )}-1 biggr }E(tilde{t},tilde{y})dtilde{y}\&le (r+zeta )(mathfrak {R}^0-1)int _{Lambda }E(tilde{t},tilde{y})dtilde{y}. end{aligned}$$

It is obvious (dfrac{dV}{dtilde{t} }<0) (forall) (tilde{t} ge 0) and (tilde{y}in Lambda) (Leftrightarrow) (mathfrak {R}^0<1). Further, (dfrac{dV}{dtilde{t} }=0) if and only if (E(tilde{t},tilde{y})rightarrow 0), then the model (1) implies that (I_1(tilde{t},tilde{y})rightarrow 0), (I_2(tilde{t},tilde{y})rightarrow 0), (I_2(tilde{t},tilde{y})rightarrow 0), (R( t,tilde{y})rightarrow 0) and (S(tilde{t},tilde{y})rightarrow dfrac{Pi }{zeta}) ((S, E, I_1, I_2, I_3, R) rightarrow left( dfrac{Pi }{zeta }, 0, 0, 0, 0, 0right)). In a result we concluded that the largest compact invariant set within the region (left{ (S, E, I_1, I_2, I_3, R): dfrac{dV}{dtilde{t}}=0 right}) is (Gamma _0). By employing the well-established LaSalles principle, (Gamma _0) is globally asymptotically stable under the condition (mathfrak {R}^0<1). (square)

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Paxlovid Fails to Shorten COVID in Standard-Risk and Vaccinated At-Risk Patients – Medpage Today

April 4, 2024

Nirmatrelvir/ritonavir (Paxlovid) failed to shorten COVID-19 symptom duration among people at standard risk for severe COVID-19 and among vaccinated people with at least one risk factor for severe disease, according to final results of the phase II/III EPIC-SR trial.

In vaccinated and unvaccinated patients with COVID-19 at standard risk for severe disease and in fully vaccinated people with at least one risk factor who took nirmatrelvir/ritonavir, the median time to alleviation of COVID-19 symptoms was 12 days, compared with 13 days in patients who took a placebo (P=0.60), reported Jennifer Hammond, PhD, development head of antivirals at Pfizer in Collegeville, Pennsylvania, and colleagues in the New England Journal of Medicine.

"The usefulness of nirmatrelvir-ritonavir in patients who are not at high risk for severe COVID-19 has not been established," the authors wrote.

There was a trend toward fewer hospitalizations and death among participants who took nirmatrelvir/ritonavir, where five (0.8%) of these participants were hospitalized for COVID-19 or died from any cause, compared with 10 (1.6%) of those in the placebo group through day 28 (95% CI 2.0 to 0.4). However, the difference did not reach statistical significance.

In a subgroup analysis of at-risk participants only, three (0.9%) of the participants receiving nirmatrelvir/ritonavir were hospitalized or died versus seven (2.2%) in the placebo group (95% CI -3.3 to 0.7).

In 2022, the drug's manufacturer Pfizer announced an updated interim analysis of the EPIC-SR that reached similar conclusions to these final results. Because of low rates of hospitalization in the study population, Pfizer ended enrollment in EPIC-SR at that time.

In contrast to EPIC-SR, the EPIC-HR trial that enrolled high-risk unvaccinated participants found that nirmatrelvir/ritonavir reduced hospitalization or death by 88%.

"What can we conclude from these two trials about nirmatrelvir-ritonavir for the treatment of COVID-19?" wrote Rajesh Gandhi, MD, and Martin Hirsch, MD, of Massachusetts General Hospital and Harvard Medical School in Boston, in an accompanying editorial.

"Clearly, the benefit observed among unvaccinated high-risk persons does not extend to those at lower risk for severe COVID-19," they noted. The EPIC-SR results add support to current recommendations that nirmatrelvir/ritonavir is only indicated for treatment of mild-to-moderate COVID-19 in persons at high risk for disease progression, they said.

However, Gandhi and Hirsch also pointed out that although the EPIC-SR trial failed to show that nirmatrelvir/ritonavir shortened COVID-19 symptoms in vaccinated participants with at least one risk factor, the study enrolled only a small percentage of patients who were most likely to be hospitalized with COVID-19.

"Other than obesity, smoking, and hypertension, risk factors for severe COVID-19 were uncommon; for example, less than 2% of the participants had heart or lung disease," they wrote. Of note, only 5% of EPIC-SR enrollees were 65 years or older.

"As with many medical interventions, there is likely to be a gradient of benefit for nirmatrelvirritonavir, with the patients at highest risk for progression most likely to derive the greatest benefit," Gandhi and Hirsch commented.

About 26% of participants who received nirmatrelvir/ritonavir experienced an adverse event, similar to the 24.1% in the placebo group. The most commonly reported treatment-related adverse events among those who took nirmatrelvir/ritonavir were dysgeusia (5.8%) and diarrhea (2.1%). In participants receiving nirmatrelvir/ritonavir, 3.7% reported grade 3 or 4 adverse events versus 3.9% in the placebo group. No grade 5 adverse events occurred in the treatment group.

Of note, symptom rebound at day 14 occurred in 11.4% of participants treated with nirmatrelvir/ritonavir versus 16.1% in the placebo group. Viral load rebound at day 14 was similar between the 2 groups (4.3% in the nirmatrelvir/ritonavir group vs 4.1% in the placebo group).

The EPIC-SR trial included 1,296 participants who had confirmed COVID-19 and symptom onset within the previous 5 days. Participants were randomized 1:1 to receive either nirmatrelvir/ritonavir (n=654) or placebo (n=634). Participants recorded COVID-19 symptoms on a daily basis from day 1 through day 28.

The primary end point of the study was time to sustained alleviation of all targeted COVID-19 signs and symptoms. COVID-19-related hospitalization and death from any cause were also assessed through day 28. A total of 1,250 participants completed efficacy and safety follow-up assessments.

Of participants, 54% were women and the median age was 42 years. The majority of participants were white (78.5%) and 41.4% were Hispanic or Latino. Approximately 57% had been vaccinated for COVID-19, and about 50% had at least one risk factor for severe COVID-19. The most common comorbidity was hypertension (12.3%) and the most common risk factor was cigarette smoking (13.3%).

Most participants (72.5%) underwent randomization within 3 days after symptom onset. Adherence to nirmatrelvir/ritonavir -- defined as having taken at least 80% of the pills -- was nearly 95%.

Katherine Kahn is a staff writer at MedPage Today, covering the infectious diseases beat. She has been a medical writer for over 15 years.

Disclosures

The study was funded by Pfizer.

Hammond is an employee of Pfizer and reported holding stock options in Pfizer. Co-authors are employees of Pfizer or reported other ties to industry.

Gandhi and Hirsch reported no disclosures.

Primary Source

New England Journal of Medicine

Source Reference: Hammond J, et al "Nirmatrelvir for vaccinated or unvaccinated adult outpatients with COVID-19" New Engl J Med 2024; DOI: 10.1056/NEJMoa2309003.

Secondary Source

New England Journal of Medicine

Source Reference: Gandhi RT, Hirsch M "Treating acute COVID-19 -- final chapters still unwritten" New Engl J Med 2024; DOI: 10.1056/NEJMe2402224.

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Paxlovid Fails to Shorten COVID in Standard-Risk and Vaccinated At-Risk Patients - Medpage Today

4 year anniversary of COVID-19: lessons learned, best protective products – Reviewed

April 4, 2024

While COVID-19 emerged in late 2019 (hence the name), its been about four years since the virus become a global pandemic. Whether you feel like it was just yesterday or a lifetime ago, a great many lessons were imparted to us throughout the experience and its up to us to do our part in learning from it and taking proper precautions in the future from viruses such as COVID, RSV, influenza, and norovirus.

While COVID-19 is no longer considered a public health emergency, its still out there. Here are things weve learned from the Centers for Disease Control and Prevention (CDC) and health professionals about COVID-19 and similar viruses.

The first American case was reported on January 20, 2020. The outbreak was officially declared a national emergency in the U.S. on March 13, 2020.

One professional we spoke to was Susan Hassig, Associate Professor Emerita of Epidemiology at the Tulane School of Public Health and Tropical Medicine. When we asked her opinion on what positive behavioral changes the pandemic brought about, she mentioned the continued use of masks in public by at-risk populations.

I suspect that many people are still being a bit cautious about going into large group settings for extended periods of time, Hassig says.

Hassigs optimism is tempered by the negative behaviors that she and other health professionals have observed, particularly when it comes to the vaccine rate not being where they would like it to be, including among at-risk populations.

Masks remain one of the most effective ways to protect yourself from getting sick with COVID-19, though it took us a bit to figure out exactly what types of masks wereand were noteffective. Various doodads such as face shields and gaiter masks were used by many in the early months of the pandemic before being phased out as their inefficacy was exposed. Cloth masks, in particular, rose in popularity due to how customizable they were, and were widely used for a year before it was revealed that they, too, arent the most effective in blocking germs.

Today's gold standard in safe masking is an N95 or a KN95 mask. The names come from their effectiveness, as they can filter 95% of particles responsible for the spread of airborne diseases. We learned through this experience that it's important to wary of fake KN95 and N95 masks. One of the best tells for fake KN95 masks is the brand advertising a mask as being NIOSH- or FDA-approved. Genuine KN95 masks will also have a serial number printed on them, beginning with the letters GB.

WWDOLL KN95 Face Mask 25 Pack

Genuine KN95 and N95 maks are the best shields against COVID-19.

We learned that one way that COVID-19 can spread is through contaminated surfaces that contain viral droplets. You can inadvertently touch these droplets and get sick by touching your hands to your face or an open cut. To avoid this, disinfectant spray can be used to clean these high-contact areas. However, its important to use a spray that is listed on the Environmental Protection Agency (EPA)s list of disinfectants that are confirmed to kill coronavirus. What's more, the disinfectant must be applied by following the instructions on the label or must sit on the surface it's being applied to for at least 10 minutes to be most effective.

Lysol Disinfectant Handi-Pack Wipes

Disinfect surfaces properly with these wipes.

As stated, COVID-19 and similar viruses can be contracted when you touch your eyes, nose, or mouth with your hands after encountering viral particles. While this can be mitigated by disinfecting surfaces, Professor Hassig prioritizes the importance of hand washing, noting that its much easier to simply clean your hands in a pinch rather than disinfect surfaces thoroughly.

The proper way to keep your hands clean is to wash them thoroughly. This involves rinsing your hands with warm water, lathering them with soap for 20 seconds (or roughly the length of time it takes to sing the Alphabet Song or Twinkle, Twinkle, Little Star, and then rinsing your hands with warm water.

Softsoap Liquid Hand Soap

Lather your hands for 20 seconds.

While the CDC prioritizes using soap and water to disinfect hands, it suggests the use of hand sanitizer when soap and water isnt available. In 2020, DIY hand sanitizer was all the rage because of the hand sanitizer shortage, but before you go and jerry-rig your own sanitizer, you should know it needs to be at least 60% alcohol. If you dont want to figure out how to ensure your own hand sanitizer is at least 60%, we suggest buying portable, pre-made hand sanitizer that will disinfect your hands in a pinch. Just read the label for its alcohol content before purchasing.

Purell Advanced Hand Sanitizer Refreshing Gel

Use hand sanitizer made with at least 60% alcohol.

The CDC recently released new COVID-19 guidelines that reduce the amount of time you need to isolate when exposed to the virus. While the time needed to isolate is shortened, Hassig stresses the importance of still isolating for either five days or as long as a person is symptomatic, which she notes will probably last around the same amount of time.

To know when to isolate, its important to know when youre displaying symptoms. One common symptom is running a fever, which is defined by the CDC as a temperature of 100.4F or higher). Other symptoms, across variants, include:

Forehead thermometer

A fever is one of the more common symptoms of COVID-19.

The only way to know for sure whether you have COVID-19 is by taking a COVID-specific test. Fortunately, in the years since the pandemic first broke out, at-home tests have become widely available.

COVID-19 Antigen Self Test

Testing is the only surefire way to determine if you have COVID-19.

Another way to filter the air you breathe is by using an air purifier. Professor Susan Hassig identifies air filters as one of the most effective ways to clean an area of COVID-19 particulates.

Its recommended by the CDC to place filters where someone is isolating or where someone high-risk resides, if applicable. In particular, they recommend the use of air purifiers with HEPA filters, as they satisfy the highest standard of efficiency when it comes to improving the air in your home.

BLUEAIR Air Purifiers

Air purifiers are one of the most effective ways to rid an area of COVID-19 particulates.

Prices were accurate at the time this article was published but may change over time.

The product experts at Reviewed have all your shopping needs covered. Follow Reviewed on Facebook, Twitter, Instagram, TikTok, or Flipboard for the latest deals, product reviews, and more.

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4 year anniversary of COVID-19: lessons learned, best protective products - Reviewed

Tracking the Evolution of COVID-19 – News Center – Feinberg News Center

April 4, 2024

Scientists have developed a machine-learning approach to track the evolution of SARSCoV2, the virus that causes COVID-19, and potentially other viruses, according to a study published in the Proceedings of the National Academy of Sciences.

Since the onset of the COVID-19 pandemic, 1,185,413 people in the United States have died from the virus, according to data collected by the Centers for Disease Control and Prevention.

RNA viruses, such as SARSCoV2, mutate rapidly once inside a host. Most RNA viruses including HIV-1 or influenza acquire a high number of mutations to the point where, in many cases, no two copies of the virus inside one person have the exact same genome.

These mutated strains can then jump to the general population, pushing forward the evolution of these viruses at a global level. While SARSCoV2 has been reported to mutate at lower rates compared to similar viruses, it has shown a high capacity to evolve with new variants appearing suddenly instead of progressively. This observation challenges the previous idea of low mutation capacity of SARS-CoV-2, said Ramon Lorenzo-Redondo, PhD, assistant professor of Medicine in the Division of Infectious Diseases and bioinformatics director of the Center for Pathogen Genomics and Microbial Evolution (CPGME), who was a co-author of the study.

The origins of these highly mutated variants such as Omicron, which acquired a very high number of mutations rapidly, is still poorly understood, he said.

In the study, Lorenzo-Redondo and his collaborators applied a novel next-generation sequencing method to sequence the genome of SARS-CoV-2 from thirty individual nasal swab samples obtained within a 19-day window.

With this new method, developed by senior study author Esteban Domingo, PhD, and study co-author Celia Perales, PhD, investigators at the Spanish National Research Council, the team was able to capture a wide representation of every mutant of the virus present inside each patient. This way, they could study if minority mutations generated inside an infected patient could be the origin of mutations that later get transferred to the general population.

Then, utilizing a machine-learning model first developed by Lorenzo-Redondo and Soledad Delgado, PhD, associate professor at the Polytechnical University of Madrid, the investigators visualized genetic data from the samples into maps which showed the many variations of the virus inside a single host and charted their predicted survival and proliferation in relation to the other variants.

The technique may allow scientists to track how viruses like SARS-CoV-2 evolve over time inside a single person and predict dangerous mutations, Lorenzo-Redondo said.

With this technique, we can go deeper. We can analyze evolution and analyze how the virus is adapting to a person and how it evolves to counter the immune system, Lorenzo Redondo said. Some of these adapted viruses then might become important at a population level.

By sequencing SARS-CoV-2 inside an individual, investigators observed how the virus tested a mutation in its spike protein in some individuals, which has been reported to alter viral entry. This specific spike protein mutant variant was a small subset inside of some of these hosts, but subsequently, it quickly overtook other variants due to its superior infectivity and became the dominant strain globally during the first months of the pandemic.

The findings may explain how new variants materialize and jump from person to person and become dominant, strengthening the entire viral population, Lorenzo Redondo said.

This is very interesting because it seems to suggest that all these big jumps that we see, for example in the Omicron wave of COVID-19, might be happening at the intra-host level in multiple patients at the same time and then get transferred to the general population, he said.

Moving forward, Lorenzo Redondo and his collaborators will aim to use this combination of novel molecular biology techniques and machine learning approaches to map intra-host evolution in SARS-CoV-2 and other viruses, he said. His group also hopes to use the approach to predict how a virus may evolve in the future and potentially stop dangerous strains from taking hold.

The next step is: can we use machine learning methods to predict possible future mutations by knowing what the virus has already explored and what type of advantage it gave it inside the host? Lorenzo Redondo said.

The study was supported by grants PID2019-104903RB-100 and PID2022-139908OB-I00 from the Spanish Ministry of Science.

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Tracking the Evolution of COVID-19 - News Center - Feinberg News Center

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