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

Abstract

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. © 2017 Elsevier Inc. All rights reserved.

Weather Conditions and COVID-19 Cases: Insights from the GCC Countries

“…According to their study, only temperature was related to an increase in the prediction accuracy, and this increase was only detected in hot countries. The findings of Iloanusi and Ross (2021) do not align with those of previous studies, which indicated that temperature and humidity must be considered together. Moreover, temperature has been shown to improve the forecasting accuracy in cold countries, such as Canada ( Khennou and Akhloufi, 2021 ) and Russia ( Pramanik et al., 2020 ), and these results were rejected by Iloanusi and Ross (2021) .…”

“…(2020) USA Shallow LSTM model UV, temperature, perception, ozone, dew, and humidity The prediction accuracy of the model was not affected by weather conditions data. Iloanusi and Ross (2021) 20 countries LSTM, random forest, Temperature, rainfall, windspeed, irradiation, humidity Temperature improved the accuracy of the forecasting models for most of the studied countries. Gupta et al.…”

“…(2020) demonstrated that the prediction accuracy of their LSTM model in forecasting COVID-19 cases, deaths, and recoveries in the USA did not improve when including weather data. Several weather parameters were included as inputs in the multi-variate LSTM model proposed by Iloanusi and Ross (2021) for predicting the COVID-19 cases-to-mortality ratio in 36 countries. According to their study, only temperature was related to an increase in the prediction accuracy, and this increase was only detected in hot countries.…”

“…Vaccination is expected to decrease COVID-19 transmission by reducing symptomatic and asymptomatic infections, and hindering the person-to-person spread of the virus ( Eyre et al., 2022 ). Moreover, as argued by Iloanusi and Ross (2021) , more than one season is required to study the effects of weather on the spread of COVID-19.

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Transformer-based Deep Learning for COVID-19 Prediction Based on Climate Variables in Indonesia

“…Several investigations have been conducted to build a COVID-19 prediction model. Iloanusi [25] developed an LSTM model and a random forest model to forecast the number of COVID-19 cases in 36 countries. They used a variety of data sources, namely COVID-19 case data from worldmeters.info and weather data obtained from the National Aeronautics and Space Administration (NASA).…”