Abstract
The spread of a disease caused by a virus can happen through human to human contact or could be from the environment. A mathematical model could be used to capture the dynamics of the disease spread to estimate the infections, recoveries, and deaths that may result from the disease. An estimation is crucial to make policy decisions and for the alerts for the medical emergencies that may arise. Many epidemiological models are being used to make such an estimation. One major factor that is important in the forecasts using the models is the dynamic nature of the disease spread. Unless we can come up with a way of estimating the parameters that guide this dynamic spread, the models may not give accurate forecasts. In this work, using the SEIRD model, attempts are made to forecast Infected, Recovered and Death rates of COVID-19 up to a week using an incremental approach. A method of optimizing the parameters of the model is also discussed thoroughly in this work. The model is evaluated using the data taken from COVID-19 India tracker [2], a crowd-sourced platform for India. The model is tested with the whole country as well as all the states and districts. The results of all the states and districts obtained from our model can be seen in [12]. Forecasts for Infected and Deaths for the whole country and the state of Maharashtra are satisfactory with an average % error rate of 3.47 and 3.60 for infected and 3.88 and 1.61 for deaths respectively. It is supposed to be a reasonable estimate which can help the governments in planning for emergencies such as ICU requirements, PPEs, hospitalizations, and so on as the infection is going to be prevalent for some time to come.
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
No funding was received for any aspect of the submitted work.
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We declare that no animals and humans are involved and that this is purely computational work.
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Footnotes
Data Availability
The data referred to in this manuscript is open-sourced and is available in the below-mentioned links.