Modelling SARS-CoV-2 unreported cases in Italy: Analysis of serological survey and vaccination scenarios

Objectives:Aim of the present paper is the study of the large unreported component,characterizing the SARS-CoV-2 epidemic event in Italy,taking advantage of the Istat survey.Particular attention is devoted to the sensitivity and specificity of the serological test and their effects.Methods:The model satisfactory reproduces the data of the Italian survey showing a relevant predictive power and relegating in a secondary position models which do not include,in the simulation,the presence of asymptomatic groups.The corrections due to the serological test sensitivity(in particular those ones depending on the symptoms onset)are crucial for a realistic analysis of the unreported(and asymptomatic)components.Results:The relevant presence of an unreported component during the second pandemic wave in Italy is confirmed and the ratio of reported to unreported cases is predicted to be roughly 1:4 in the last months of year 2020.A method to correct the serological data on the basis of the antibody sensitivity is suggested and systematically applied.The asymptomatic component is also studied in some detail and its amount quantified.A model analyses of the vaccination scenarios is performed confirming the relevance of a massive campaign(at least 80000 immunized per day)during the first six months of the year 2021,to obtain important immunization effects within August/September 2021.

Mathematical Model of the Spread of the Coronavirus Disease 2019 (COVID-19) in Burkina Faso

In this paper, we develop a mathematical model of the COVID-19 pandemic in Burkina Faso. We use real data from Burkina Faso National Health Commission against COVID-19 to predict the dynamic of the disease and also the cumulative number of reported cases. We use public policies in model in order to reduce the contact rate, this allows to show how the reduction of the daily report of infectious cases goes, so we would like to draw the attention of decision makers for a rapid treatment of reported cases.

A COVID-19 epidemic model with latency period

At the beginning of a COVID-19 infection,there is a period of time known as the exposed or latency period,before an infected person is capable of transmitting the infection to another person.We develop two differential equations models to account for this period.The first is a model that incorporates infected persons in the exposed class,before transmission is possible.The second is a model that incorporates a time delay in infected persons,before transmission is possible.We apply both models to the COVID-19 epidemic in China.We estimate the epidemiological parameters in the models,such as the transmission rate and the basic reproductive number,using data of reported cases.We thus evaluate the role of the exposed or latency period in the dynamics of a COVID-19 epidemic.

Clarifying predictions for COVID-19 from testing data: The example of New York State

With the spread of COVID-19 across the world,a large amount of data on reported cases has become available.We are studying here a potential bias induced by the daily number of tests which may be insufficient or vary over time.Indeed,tests are hard to produce at the early stage of the epidemic and can therefore be a limiting factor in the detection of cases.Such a limitation may have a strong impact on the reported cases data.Indeed,some cases may be missing from the official count because the number of tests was not sufficient on a given day.In this work,we propose a new differential equation epidemic model which uses the daily number of tests as an input.We obtain a good agreement between the model simulations and the reported cases data coming from the state of New York.We also explore the relationship between the dynamic of the number of tests and the dynamics of the cases.We obtain a good match between the data and the outcome of the model.Finally,by multiplying the number of tests by 2,5,10,and 100 we explore the consequences for the number of reported cases.