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Climate influences on COVID‐19 prevalence rates: An application of a panel data spatial model

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  • Joebson Maurilio Alves dos Santos
  • Tatiane Almeida de Menezes
  • Rodrigo Gomes de Arruda
  • Flávia Emília Cavalcante Valença Fernandes

Abstract

The present study aims to measure the impact of climate characteristics on the prevalence rate of coronavirus disease 2019 (COVID‐19) in Brazilian states given the exogenous nature of these variables. We used a daily panel for the period from March 10 to April 10, 2020, the first phase of the pandemic, as there were few intervention policies to contain the spread of COVID‐19 during that period, and it was estimated through generalized least squares (GLS) spatial models to control the presence of spatial spillover, first‐order autoregressive errors, and correlation between cross‐sections. Considering the COVID‐19 incubation period and the time it takes for COVID‐19 symptoms to manifest, the econometric models were estimated using the 14‐, 11‐, and 7‐day moving averages of the climate variables. The results showed that increases of 1% in the solar incidence, average temperature, and relative humidity of the air reduced COVID‐19 prevalence rates by 0.16%, 0.049%, and 0.22%, respectively, considering the 11‐day moving average. El presente estudio tiene como objetivo medir el impacto de las características climáticas en la prevalencia de la enfermedad por coronavirus 2019 (COVID‐19) en los estados brasileños, dada la naturaleza exógena de estas variables. Se utilizó un panel diario para el período comprendido entre el 10 de marzo y el 10 de abril de 2020, la primera fase de la pandemia, ya que hubo pocas políticas de intervención para contener la propagación de COVID‐19 durante ese período, y se estimó mediante modelos espaciales de mínimos cuadrados generalizados (GLS) para controlar la presencia de spillover espacial, errores autorregresivos de primer orden y la correlación entre muestras representativas. Teniendo en cuenta el periodo de incubación de COVID‐19 y el tiempo que tardan en manifestarse los síntomas de COVID‐19, los modelos econométricos se estimaron utilizando las medias móviles de 14, 11 y 7 días de las variables climáticas. Los resultados mostraron que aumentos del 1% en la incidencia solar, la temperatura media y la humedad relativa del aire redujeron la prevalencia de COVID‐19 en un 0,16%, 0,049% y 0,22%, respectivamente, teniendo en cuenta la media móvil de 11 días. 本稿では、ブラジルの各州における新型コロナウイルス感染症 (COVID‐19)の罹患率に対する気候特性の影響を、これらの変数の外因性を考慮して、測定する。パンデミックの第一波である2020年3月10日~4月10日の期間は、COVID‐19の拡散を封じ込めるための介入政策がほとんどなかったが、この期間の毎日のパネルデータを使用し、一般化最小二乗法 (GLS)による空間モデルを用いて、空間スピルオーバー、1次自己回帰のエラー、および横断面間の相関の存在を制御して、推定した。COVID‐19の潜伏期間と発症までにかかる時間を考慮し、気候変数の14、11、7日間の移動平均を用いて計量経済モデルを推定した。結果から、11日間の移動平均を考慮すると、太陽光の入射、平均気温、空気中の相対湿度の1%増加が、それぞれCOVID‐19罹患率を0.16%、 0.049%、0.22%減少させることが示された。

Suggested Citation

  • Joebson Maurilio Alves dos Santos & Tatiane Almeida de Menezes & Rodrigo Gomes de Arruda & Flávia Emília Cavalcante Valença Fernandes, 2023. "Climate influences on COVID‐19 prevalence rates: An application of a panel data spatial model," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(3), pages 456-473, April.
    • Handle: RePEc:bla:rgscpp:v:15:y:2023:i:3:p:456-473
    DOI: 10.1111/rsp3.12504
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