The COVID-19 pandemic,caused by SARS-CoV-2,highlighted heterogeneities in human behavior and attitudes of individuals with respect to adherence or lack thereof to public health-mandated intervention and mitigation mea...The COVID-19 pandemic,caused by SARS-CoV-2,highlighted heterogeneities in human behavior and attitudes of individuals with respect to adherence or lack thereof to public health-mandated intervention and mitigation measures.This study is based on using mathematical modeling approaches,backed by data analytics and computation,to theoretically assess the impact of human behavioral changes on the trajectory,burden,and control of the COVID-19 pandemic during the first two waves in New York City.A novel behavior-epidemiology model,which considers n heterogeneous behavioral groups based on level of risk tolerance and distinguishes behavioral changes by social and disease-related motivations(such as peer-influence and fear of disease-related hospitalizations),is developed.In addition to rigorously analyzing the basic qualitative features of this model,a special case is considered where the total population is stratified into two groups:risk-averse(Group 1)and risk-tolerant(Group 2).The 2-group model was calibrated and validated using daily hospitalization data for New York City during the first wave,and the calibrated model was used to predict the data for the second wave.The 2-group model predicts the daily hospitalizations during the second wave almost perfectly,compared to the version without behavioral considerations,which fails to accurately predict the second wave.This suggests that epidemic models of the COVID-19 pandemic that do not explicitly account for heterogeneities in human behavior may fail to accurately predict the trajectory and burden of the pandemic in a population.Numerical simulations of the calibrated 2-group behavior model showed that while the dynamics of the COVID-19 pandemic during the first wave was largely influenced by the behavior of the risk-tolerant(Group 2)individuals,the dynamics during the second wave was influenced by the behavior of individuals in both groups.It was also shown that disease-motivated behavioral changes(i.e.,behavior changes due to the level of COVID-19 hospitalizations in the community)had greater influence in significantly reducing COVID-19 morbidity and mortality than behavior changes due to the level of peer or social influence or pressure.Finally,it is shown that the initial proportion of members in the community that are risk-averse(i.e.,the proportion of individuals in Group 1 at the beginning of the pandemic)and the early and effective implementation of non-pharmaceutical interventions have major impacts in reducing the size and burden of the pandemic(particularly the total COVID-19 mortality in New York City during the second wave).展开更多
The African meningitis belt(AMB)faces recurring epidemics of Neisseria meningitidis(Nm)(a bacterium that causes meningococcal meningitis),posing significant public health challenges.This study develops a Susceptible-C...The African meningitis belt(AMB)faces recurring epidemics of Neisseria meningitidis(Nm)(a bacterium that causes meningococcal meningitis),posing significant public health challenges.This study develops a Susceptible-Carrier-Infected-Recovered(SCIR)-based dynamic model to investigate Nm transmission dynamics in the AMB region,focussing on the impact of pentavalent meningococcal conjugate vaccines targeting serogroups A,C,W-135,Y,and X.By incoporating vaccination strategies into the model,we provide a comprehensive framework for evaluating vaccine effectiveness and informing outbreak prevention and control efforts.Our model stratifies the population into high-risk individuals(ages 1–29 years),who are the primary targets of vaccination campaigns,and low-risk individuals(all other age groups),capturing differences in susceptibility and vaccine coverage.Our results reveal that the introduction of pentavalent vaccines significantly reduces the prevalence of carriers,particularly among high-risk groups,thereby curbing transmission and mitigating epidemic risks across the AMB region.Key epidemiological parameters,including reproduction numbers(R0),are derived to support targeted intervention strategies.Further analysis highlights the role of vaccination in lowering Nm transmissibility,especially in densely populated settings where close contact accelerates spread.Moreover,potential drivers of Nm outbreaks,including climate variability,socioeconomic disparities,and population density,are identified,highlighting the need for integrated public health intervention strategies.Further simulations also reveal the effectiveness of pentavalent vaccination among high-risk populations;however,further research is urgently needed to understand disease heterogeneity and vulnerability,particularly in young children and underserved communities.Thus,this study contribute to advancing our understanding of effective and sustainable vaccination strategies and enhancing epidemic preparedness in meningitis-endemic regions.展开更多
基金ABG acknowledges the support,in part,of the National Science Foundation(Grant Number:DMS-2052363,transferred to DMS-2330801)AO acknowledges the support of the University of Maryland School of Graduate Studies Dean's Fellowship.
文摘The COVID-19 pandemic,caused by SARS-CoV-2,highlighted heterogeneities in human behavior and attitudes of individuals with respect to adherence or lack thereof to public health-mandated intervention and mitigation measures.This study is based on using mathematical modeling approaches,backed by data analytics and computation,to theoretically assess the impact of human behavioral changes on the trajectory,burden,and control of the COVID-19 pandemic during the first two waves in New York City.A novel behavior-epidemiology model,which considers n heterogeneous behavioral groups based on level of risk tolerance and distinguishes behavioral changes by social and disease-related motivations(such as peer-influence and fear of disease-related hospitalizations),is developed.In addition to rigorously analyzing the basic qualitative features of this model,a special case is considered where the total population is stratified into two groups:risk-averse(Group 1)and risk-tolerant(Group 2).The 2-group model was calibrated and validated using daily hospitalization data for New York City during the first wave,and the calibrated model was used to predict the data for the second wave.The 2-group model predicts the daily hospitalizations during the second wave almost perfectly,compared to the version without behavioral considerations,which fails to accurately predict the second wave.This suggests that epidemic models of the COVID-19 pandemic that do not explicitly account for heterogeneities in human behavior may fail to accurately predict the trajectory and burden of the pandemic in a population.Numerical simulations of the calibrated 2-group behavior model showed that while the dynamics of the COVID-19 pandemic during the first wave was largely influenced by the behavior of the risk-tolerant(Group 2)individuals,the dynamics during the second wave was influenced by the behavior of individuals in both groups.It was also shown that disease-motivated behavioral changes(i.e.,behavior changes due to the level of COVID-19 hospitalizations in the community)had greater influence in significantly reducing COVID-19 morbidity and mortality than behavior changes due to the level of peer or social influence or pressure.Finally,it is shown that the initial proportion of members in the community that are risk-averse(i.e.,the proportion of individuals in Group 1 at the beginning of the pandemic)and the early and effective implementation of non-pharmaceutical interventions have major impacts in reducing the size and burden of the pandemic(particularly the total COVID-19 mortality in New York City during the second wave).
文摘The African meningitis belt(AMB)faces recurring epidemics of Neisseria meningitidis(Nm)(a bacterium that causes meningococcal meningitis),posing significant public health challenges.This study develops a Susceptible-Carrier-Infected-Recovered(SCIR)-based dynamic model to investigate Nm transmission dynamics in the AMB region,focussing on the impact of pentavalent meningococcal conjugate vaccines targeting serogroups A,C,W-135,Y,and X.By incoporating vaccination strategies into the model,we provide a comprehensive framework for evaluating vaccine effectiveness and informing outbreak prevention and control efforts.Our model stratifies the population into high-risk individuals(ages 1–29 years),who are the primary targets of vaccination campaigns,and low-risk individuals(all other age groups),capturing differences in susceptibility and vaccine coverage.Our results reveal that the introduction of pentavalent vaccines significantly reduces the prevalence of carriers,particularly among high-risk groups,thereby curbing transmission and mitigating epidemic risks across the AMB region.Key epidemiological parameters,including reproduction numbers(R0),are derived to support targeted intervention strategies.Further analysis highlights the role of vaccination in lowering Nm transmissibility,especially in densely populated settings where close contact accelerates spread.Moreover,potential drivers of Nm outbreaks,including climate variability,socioeconomic disparities,and population density,are identified,highlighting the need for integrated public health intervention strategies.Further simulations also reveal the effectiveness of pentavalent vaccination among high-risk populations;however,further research is urgently needed to understand disease heterogeneity and vulnerability,particularly in young children and underserved communities.Thus,this study contribute to advancing our understanding of effective and sustainable vaccination strategies and enhancing epidemic preparedness in meningitis-endemic regions.
