Online analysis of epidemics with variable infection rate

TL;DR

This paper introduces a discrete-time epidemiological model called HIT for analyzing COVID-19 spread, utilizing a new infection rate indicator to distinguish epidemic phases and assess the impact of political decisions.

Contribution

The paper develops the HIT model and proposes a novel total infection rate indicator for real-time epidemic analysis and prediction.

Findings

The indicator accurately detects epidemic phases across multiple countries.

Political decisions based solely on new case numbers are often incorrect.

Most countries analyzed are in a dangerous epidemic zone, except Sweden.

Abstract

In this paper, we continue development of the new epidemiological model HIT, which is suitable for analyzing and predicting the propagation of COVID-19 epidemics. This is a discrete-time model allowing a reconstruction of the dynamics of asymptomatic virus holders using the available daily statistics on the number of new cases. We suggest to use a new indicator, the total infection rate, to distinguish the propagation and recession modes of the epidemic. We check our indicator on the available data for eleven different countries and for the whole world. Our reconstructions are very precise. In several cases, we are able to detect the exact dates of the disastrous political decisions, ensuring the second wave of the epidemics. It appears that for all our examples the decisions made on the basis of the current number of new cases are wrong. In this paper, we suggest a reasonable alternative. Our analysis shows that all tested countries are in a dangerous zone except Sweden.