Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19

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There are several recent publications criticizing the failure of COVID-19 forecasting models, with swinging over predictions and underpredictions, which have made it difficult for decision and policy making. Observing the failures of several COVID-19 forecasting models and the alarming spread of the virus, we seek to use some stable response for forecasting COVID-19, viz., ratios of COVID-19 cases to mortalities, rather than COVID-19 cases or fatalities. A trend of low COVID-19 cases-to-mortality ratios calls for urgent attention: the need for vaccines, for instance. Studies have shown that there are influences of weather parameters on COVID-19; and COVID-19 may have come to stay and could manifest a seasonal outbreak profile similar to other infectious respiratory diseases. In this paper, the influences of some weather, geographical, economic and demographic covariates were evaluated on COVID-19 response based on a series of Granger-causality tests. The effect of four weather parameters, viz., temperature, rainfall, solar irradiation and relative humidity, on daily COVID-19 cases-to-mortality ratios of 36 countries from 5 continents of the world were determined through regression analysis. Regression studies show that these four weather factors impact ratios of COVID-19 cases-to-mortality differently. The most impactful factor is temperature which is positively correlated with COVID-19 cases-to-mortality responses in 24 out of 36 countries. Temperature minimally affects COVID-19 cases-to-mortality ratios in the tropical countries. The most influential weather factor - temperature - was incorporated in training random forest and deep learning models for forecasting the cases-to-mortality rate of COVID-19 in clusters of countries in the world with similar weather conditions. Evaluation of trained forecasting models incorporating temperature features show better performance compared to a similar set of models trained without temperature features. This implies that COVID-19 forecasting models will predict more accurately if temperature features are factored in, especially for temperate countries.

Medienart:	E-Artikel

Erscheinungsjahr:	2021
Erschienen:	2021

Enthalten in:	Zur Gesamtaufnahme - volume:152
Enthalten in:	Chaos, solitons, and fractals - 152(2021) vom: 01. Nov., Seite 111340

Sprache:	Englisch

Beteiligte Personen:	Iloanusi, Ogechukwu [VerfasserIn] Ross, Arun [VerfasserIn]

Links:	Volltext

Themen:	COVID-19 COVID-19 cases-to-mortality ratios Deep learning Forecasting Journal Article Rainfall Random forest Regression analysis Relative humidity Solar irradiation Temperature Weather conditions

Anmerkungen:	Date Revised 21.12.2022 published: Print-Electronic Citation Status PubMed-not-MEDLINE

doi:	10.1016/j.chaos.2021.111340

funding:
Förderinstitution / Projekttitel:

PPN (Katalog-ID):	NLM329645897