Effects of Location-Specific Meteorological Factors on COVID-19 Daily Infection in a Tropical Climate: A Case of Kuala Lumpur, Malaysia

Abstract

Insufficient information on the novel coronavirus (COVID-19) has made it more difficult for the world to tackle its continuous implosion. Meteorological and environmental factors, in both laboratory and epidemiological studies, have been reported to affect the survival and transmission of the virus. In this study, the possible effects of location-specific meteorological parameters in a tropical climate on new daily COVID-19 infection (NDI) are investigated in Kuala Lumpur from 14 March 2020 to 31 August 2020. A generalized additive model (GAM) was imposed on ambient temperature (T) and absolute humidity (AH) to explore their nonlinear relationship with NDI. Piecewise linear regression was then used to further discern the relationships below and above the threshold values of both T and AH. The relationship between T and NDI, which was linear and statistically significant for T > 29.7°C, showed that each unit rise in temperature increases NDI by about 3.210% (CI: 1.372–7.976). AH had a more pronounced linear association with NDI for AH ≤ 22.6 g/m3 but tended to flatten the exposure-response curve above this value. A 1 g/m3 increase in AH increases NDI by 3.807% (CI: 2.064–5.732). Generally, the results indicated a positive association between T and NDI, particularly above 29.7°C, while the association with AH showed a stronger positive relationship below 22.6 g/m3. The implication of this is that COVID-19 could not be suppressed on account of warmer weather as such public health interventions remain imperative.

Effect of total population, population density and weighted population density on the spread of Covid-19 in Malaysia

“…Some geographical factors significantly influence the daily Covid-19 new cases in some of Malaysia’s states/federal territories. For instance, the temperature was positively associated with the Covid-19 spreading rate [ 46 ] as well as the absolute humidity showed a significant positive association with the spreading rate [ 46 ]. Better air quality affected by wind speed in the area also reduced spreading rates, which is particularly evident in coastal areas of Malaysia [ 47 ].…”

Univariate and Multivariate Long Short Term Memory (LSTM) Model to Predict Covid-19 Cases in Malaysia Using Integrated Meteorological Data

“…This paper enhances the above study by employing a deep learning algorithm and covers all states in Malaysia. 10.11113/mjfas.v19n4.2814…”

“…The proposed LSTM was implemented using on Keras platform (Figure 1). 10.11113/mjfas.v19n4.2814 The LSTM network block diagram of multiple basic inputs and memory transformations between each LSTM cell is presented in Figures 1a and b. LSTM cells are made up of three doors: input door (input gate, it), forget door (forget gate, ft) and output door (output gate, ot) with different functions shown in Figure 1c.…”

“…Therefore, these attributes will be removed from developing the daily COVID-19 active case prediction model. 10.11113/mjfas.v19n4.2814…”

Evaluation of the Effect of Rainfall on Covid-19 Transmission in Terengganu, Malaysia

“…The World Health Organization (WHO, 2020) designated COVID-19 as a worldwide pandemic infection in March of that year. Although it is well-accepted that the virus is transferred by human droplets in the air we breathe and by direct touch, the capability of COVID-19 aerosol transmission and the role of weather conditions are frequently debated (Makama & Lim, 2021;Beggs & Avital, 2021;Makama & Lim, 2021).…”