Use of alternative data: High frequency readout of the situation -- COVID policies, mobility, and R-Number

TL;DR

This paper explores how alternative data sources can be used to monitor COVID-19 policies, mobility, and the reproduction number in Nepal, especially during periods of limited physical data collection.

Contribution

It demonstrates the utility of high-frequency alternative data in assessing COVID-19 transmissibility and policy impact in a resource-constrained setting.

Findings

Alternative data provides timely insights into COVID-19 spread.

Mobility patterns correlate with changes in R-value.

Policy measures influence population behavior and transmission.

Abstract

Alternative data have a big role, especially during a crisis. The months of stalemate have made us realize their importance for policy responses. In Nepal, the Government has exerted stay put measures, and physical data collection activities are suspended. The confirmed cases of COVID-19 have been increasing steadily and the country is on high alert. In this impasse, the number of secondary cases one would produce over the course of outbreak -- the reproduction number is useful to monitor the transmissibility of COVID-19. As the R-value is rapidly changing, it can be affected by a range of factors, including not just how infectious a disease is but how Government responds to it, and how the population behaves. The World Health Organization (WHO) has suggested to the Government of Nepal several recommendations to contain the further spread of COVID-19. To get a sense of how Nepal is coping with the coronavirus pandemic we look at the alternative data sets to get a better understanding of the pandemic policies, mobility, and R-value during COVID.