nahomneg/modelling-spread-COVID-19-Ethiopia

This project quantifies the Impact of socio-political incidents and measures taken by the gov't on the spread of COVID-19 in Ethiopia ## Impact of socio-political incidents on the spread of COVID-19 (Ethiopia) ## Business Need Ethiopia, like many other countries across the globe, is faced with the challenge of mitigating and controlling a highly transmittable disease in the name of COVID-19. As COVID-19 is rapidly spreading rapidly, short-term modeling forecasts provide time-critical information for decisions on containment and mitigation strategies. ## Objectives The project uses a combination of an established epidemiological model (SIR) and Bayesian inference. It analyzes the time dependence of the effective growth rate of new infections and predicts the number of new cases until Aug 16. ## Procedure The model works by first taking government interventions and political instabilities that might have an effect to the transmission rate into consideration. Then it creates time intervals that correspond to the incidents. This is followed by making use of the confirmed COVID19 cases and trying to come up with the best transmission and recovery rates that correspond to the data in each time interval. This is achieved by making use of a technique called Bayesian Inference. Once we have the approximation of the transmission rate, it is possible to forecast the number of new or total cases. Our model works by adapting a science paper named Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions published by Jonas Dehning Et Al. ## Limitations The analysis has limitations arising from both the shortcomings of SIR and the recorded data. SIR requires well mixed, homogeneous populations. In a real population, infection rates and times vary with age. The model we used does not account for the time a person is infected but not able to transmit the disease (Incubation Period). The following are some of the problems that have a significant negative impact on the reliability of the data. - Ethiopia, as a developing country is struggling with lack of equipped diagnostics …