The influence of climate factors on COVID-19 transmission in Malaysia: An autoregressive integrated moving average (ARIMA) model

  1. Cho Naing
  2. Han Ni
  3. Htar Htar Aung
  4. Elaine Chan
  5. Joon Wah Mak

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Abstract

Background

A unique concern pertaining to the spread of COVID-19 across countries is the asymmetry of risk and the irrational fear of a new pandemic and its possible serious consequences. This study aimed to perform a time series analysis on the association between climate factors and daily cases of COVID-19 in Malaysia up to 15 July 2020. The second objective was to predict daily new cases using a forecasting technique. To address within-country variations, the analysis was extended to the state level with Sarawak state as an example.

Methodology/Principal Findings

Datasets on the daily confirmed cases and climate variables in Malaysia and Sarawak state were obtained from publicly accessible official websites. A descriptive analysis was performed to characterize all the important variables over the study period. An autoregressive integrated moving average (ARIMA) model was introduced using daily cases as the dependent variable and climate parameters as the explanatory variables.

For Malaysia, the findings suggest that, ceteris paribus , the number of COVID-19 cases decreased with increasing average temperature (p=0.003) or wind speed (p=0.029). However, none of the climate parameters showed a significant relationship with the number of COVID-19 cases in Sarawak state. Forecasts from the ARIMA models showed that new daily COVID-19 cases had already reached the outbreak level and a decreasing trend in both settings. Holding other parameters constant, a small number of new cases (approximately a single digit) is a probable second wave in Sarawak state,

Conclusions/Significance

The findings suggest that climate parameters and forecasts are helpful for reducing the uncertainty in the severity of future COVID-19 transmission. A highlight is that forecasts will be a useful tool for making decisions and taking the appropriate interventions to contain the spread of the virus in the community.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.14.20175372: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The basic ARIMA forecasting estimated equation is

    where STATA 16.1 (StataCorp, Texas, USA) was used to calculate the descriptive statistics, and the “Forecast” package in R (R Core team) was used to forecast the confirmed COVID-19 cases.

    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Study limitations: There were limitations to the current analyses that need to be acknowledged. As the results are based on aggregate data for the country and specific to only one particular state, in light of geographic differences, the evaluation power could have been affected. Additionally, the results may have been affected by the quality of the data in relation to the accuracy of the diagnostic tests for COVID-19. Furthermore, there might be other factors (e.g., the quality of the case surveillance system) other than weather variables that can influence the transmission potential of COVID-19. Only a few weather variables were available for inclusion in the current study. Nevertheless, there are strengths in the current study. The source data for COVID-19 cases originated from the national database, which is identical to the John Hopkins’s database, thus ensuring the accuracy of the data. The findings were, to a certain extent, comparable to those from other published studies. For instance, an inverse relationship between the average temperature and the number of new cases was in line with the findings from other countries. As we hope the presented forecasts clearly show, epidemic growth is a highly nonlinear process, where every day lost to inaction is too much [17]. On the other hand, a small spike (approximately a single digit) was seen in Sarawak state; this finding cannot rule out the possibility of new (small intensity) waves. This finding implies a potential small ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  2. Version published to 10.1101/2020.08.14.20175372v1 on medRxiv