Mathematical modeling of COVID-19 epidemic with effect of awareness programs

Severe acute respiratory syndrome coronavirus 2(SARS-COV-2)is a novel virus that emerged in China in late 2019 and caused a pandemic of coronavirus disease 2019(COVID-19).The epidemic has largely been controlled in China since March 2020,but continues to inflict severe public health and socioeconomic burden in other parts of the world.One of the major reasons for China’s success for the fight against the epidemic is the effectiveness of its health care system and enlightenment(awareness)programs which play a vital role in the control of the COVID-19 pandemic.Nigeria is currently witnessing a rapid increase of the epidemic likely due to its unsatisfactory health care system and inadequate awareness programs.In this paper,we propose a mathematical model to study the transmission dynamics of COVID-19 in Nigeria.Our model incorporates awareness programs and different hospitalization strategies for mild and severe cases,to assess the effect of public awareness on the dynamics of COVID-19 infection.We fit the model to the cumulative number of confirmed COVID-19 cases in Nigeria from 29 March to 12 June 2020.We find that the epidemic could increase if awareness programs are not properly adopted.We presumed that the effect of awareness programs could be estimated.Further,our results suggest that the awareness programs and timely hospitalization of active cases are essential tools for effective control and mitigation of COVID-19 pandemic in Nigeria and beyond.Finally,we perform sensitive analysis to point out the key parameters that should be considered to effectively control the epidemic.

Infection fatality rate and infection attack rate of COVID-19 in South American countries

Background:The ongoing COVID-19 pandemic hit South America badly with multiple waves.Different COVID-19 variants have been storming across the region,leading to more severe infections and deaths even in places with high vaccination coverage.This study aims to assess the spatiotemporal variability of the COVID-19 pandemic and estimate the infection fatality rate(IFR),infection attack rate(IAR)and reproduction number(R0)for twelve most affected South American countries.Methods:We fit a susceptible-exposed-infectious-recovered(SEIR)-based model with a time-varying transmission rate to the reported COVID-19 deaths for the twelve South American countries with the highest mortalities.Most of the epidemiological datasets analysed in this work are retrieved from the disease surveillance systems by the World Health Organization,Johns Hopkins Coronavirus Resource Center and Our World in Data.We investigate the COVID-19 mortalities in these countries,which could represent the situation for the overall South American region.We employ COVID-19 dynamic model with-and-without vaccination considering time-varying flexible transmission rate to estimate IFR,IAR and R0 of COVID-19 for the South American countries.Results:We simulate the model in each scenario under suitable parameter settings and yield biologically reasonable estimates for IFR(varies between 0.303% and 0.723%),IAR(varies between 0.03 and 0.784)and R0(varies between 0.7 and 2.5)for the 12 South American countries.We observe that the severity,dynamical patterns of deaths and time-varying transmission rates among the countries are highly heterogeneous.Further analysis of the model with the effect of vaccination highlights that increasing the vaccination rate could help suppress the pandemic in South America.Conclusions:This study reveals possible reasons for the two waves of COVID-19 outbreaks in South America.We observed reductions in the transmission rate corresponding to each wave plausibly due to improvement in nonpharmaceutical interventions measures and human protective behavioral reaction to recent deaths.Thus,strategies coupling social distancing and vaccination could substantially suppress the mortality rate of COVID-19 in South America.

Mathematical modeling and analysis of meningococcal meningitis transmission dynamics

Meningococcal meningitis(MCM)is one of the serious public health threats in the tropical and sub-tropical regions.In this paper,we propose an epidemic model to study the transmission dynamics of MCM with high-and low-risk susceptible populations.The model considers two different groups of susceptible individuals depending on the availability of medical resources(MR,including hospitals,health workers,etc.),which varies the infection risk.We find that the model exhibits the phenomenon of backward bifurcation(BB),which increases the difficulty of MCM control since the dynamics are not merely relying on the basic reproduction number,TZo.This study explores the effects of MR on the MCM epidemics by mathematical analysis and shows the existence of BB on MCM disease.Our findings suggest that providing adequate MR in a community is crucial in mitigating MCM incidences and deaths,especially,in the MCM endemic regions.