First, these data are collected after the outbreak in China. At that time, the Chinese government implemented strict lock-down. Thus the data may not be used for reference. Also, they're much fewer cases reported in the tropical area. But that's not because there are truly fewer cases. Instead, maybe those underdeveloped countries in the tropical area don't have enough methods to diagnose and report it. All these variables may affect those existing models untenable (Mecenas et al., 2020).

Defects of These Researches

These scientists performed a Risk Bias Assessment and found few (2) studies have a high risk of bias, some (11) studies have a moderate risk of bias, and four studies were classified as low-risk bias. Even if they found that not all researches are completely convincible, they acknowledge that weather and humidity should have great value in the research of COVID-19.

Ohio State University, Columbus

Imagine we are investigating two specific objects. In our intuition or subconsciousness, we assume there's some specific relationship between them. Yet, it is possible that there's no correlation at all. In another research, Mecenas et al. Collected data and articles from different journals and websites since the outbreak of COVID, including PubMed, Scopus, Web of Science, Cochrane Library, LILACS, OpenGrey, and Google Scholar. 

Analysis of Bias

Paulo Mecenas, et al. "Effects of Temperature and Humidity on the Spread of Covid-19: A Systematic Review." Plos One, vol. 15, no. 9, 2020. INSERT-MISSING-DATABASE-NAME, INSERT-MISSING-SERVICE-NAME, doi:10.1371/journal.pone.0238339. Accessed 14 June 2021.

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