Probabilistic Modelling of COVID-19 Dynamic in the Context of Madagascar

We propose a probabilistic approach to modelling the propagation of the coronavirus disease 2019 (COVID-19) in Madagascar, with all its specificities. With the strategy of the Malagasy state, which consists of isolating all suspected cases and hospitalized confirmed case, we get an epidemic model with seven compartments: susceptible (S), Exposed (E), Infected (I), Asymptomatic (A), Hospitalized (H), Cured (C) and Death (D). In addition to the classical deterministic models used in epidemiology, the stochastic model offers a natural representation of the evolution of the COVID-19 epidemic. We inferred the models with the official data provided by the COVID-19 Command Center (CCO) of Madagascar, between March and August 2020. The basic reproduction number R0 and the other parameters were estimated with a Bayesian approach. We developed an algorithm that allows having a temporal estimate of this number with confidence intervals. The estimated values are slightly lower than the international references. Generally, we were able to obtain a simple but effective model to describe the spread of the disease.

Impact of human mobility on the epidemic spread during holidays

COVID-19 has posed formidable challenges as a significant global health crisis.Its complexity stems from factors like viral contagiousness,population density,social behaviors,governmental regulations,and environmental conditions,with interpersonal interactions and large-scale activities being particularly pivotal.To unravel these complexities,we used a modified SEIR epidemiological model to simulate various outbreak scenarios during the holiday season,incorporating both inter-regional and intra-regional human mobility effects into the parameterization scheme.In addition,evaluation metrics were used to evaluate the accuracy of the model simulation by comparing the congruence between simulated results and recorded confirmed cases.The findings suggested that intra-city mobility led to an average surge of 57.35%in confirmed cases of China,while inter-city mobility contributed to an average increase of 15.18%.In the simulation for Tianjin,China,a one-week delay in human mobility attenuated the peak number of cases by 34.47%and postponed the peak time by 6 days.The simulation for the United States revealed that human mobility played a more pronounced part in the outbreak,with a notable disparity in peak cases when mobility was considered.This study highlights that while inter-regional mobility acted as a trigger for the epidemic spread,the diffusion effect of intra-regional mobility was primarily responsible for the outbreak.We have a better understanding on how human mobility and infectious disease epidemics interact,and provide empirical evidence that could contribute to disease prevention and control measures.