Estimating the growth rate of infection during the early phase of a pandemic like COVID-19

At the very outbreak of a pandemic, it is very important to be able to assess the spreading rate of the disease i.e., the rate of increase of infected people in a specific locality. Combating the pandemic situation critically depends on an early and correct prediction of, to what extent the disease may possibly grow within a short period of time. This paper attempts to estimate the spreading rate by counting the total number of infected persons at times. Adaptive clustering is especially suitable for forming clusters of infected persons distributed spatially in a locality and successive sampling is used to measure the growth in number of infected persons. We have formulated a ‘chain ratio to regression type estimator of population total’ in two occasions adaptive cluster successive sampling and studied the properties of the estimator. The efficacy of the proposed strategy is demonstrated through simulation technique as well as real life population which is followed by suitable recommendation.