Spatial Dynamics and Multiscale Regression Modelling of Population Level Indicators for COVID-19 Spread in Malaysia

Abstract

As COVID-19 dispersion occurs at different levels of gradients across geographies, the application of spatiotemporal science via computational methods can provide valuable insights to direct available resources and targeted interventions for transmission control. This ecological-correlation study evaluates the spatial dispersion of COVID-19 and its temporal relationships with crucial demographic and socioeconomic determinants in Malaysia, utilizing secondary data sources from public domains. By aggregating 51,476 real-time active COVID-19 case-data between 22 January 2021 and 4 February 2021 to district-level administrative units, the incidence, global and local Moran indexes were calculated. Spatial autoregressive models (SAR) complemented with geographical weighted regression (GWR) analyses were executed to determine potential demographic and socioeconomic indicators for COVID-19 spread in Malaysia. Highest active case counts were based in the Central, Southern and parts of East Malaysia regions of Malaysia. Countrywide global Moran index was 0.431 (p = 0.001), indicated a positive spatial autocorrelation of high standards within districts. The local Moran index identified spatial clusters of the main high–high patterns in the Central and Southern regions, and the main low–low clusters in the East Coast and East Malaysia regions. The GWR model, the best fit model, affirmed that COVID-19 spread in Malaysia was likely to be caused by population density (β coefficient weights = 0.269), followed by average household income per capita (β coefficient weights = 0.254) and GINI coefficient (β coefficient weights = 0.207). The current study concluded that the spread of COVID-19 was concentrated mostly in the Central and Southern regions of Malaysia. Population’s average household income per capita, GINI coefficient and population density were important indicators likely to cause the spread amongst communities.

The correlation between attack rates and urban health indicators during the third wave of the COVID-19 outbreak in Turkey

“…The authors explained this paradox because health care capacity can be used as a measure of a country's wealth. Unlike these results, a recent Malaysian study found no significant relationship between COVID-19 incidence and coverage of primary healthcare services ( 29 ). However, the same research explained the low number of cases in regions with low per capita income and a significant GINI coefficient, with insufficient access to health services.…”

“…Second, implementing a policy of mass testing by the government in industry and factories, and even in large nonindustrial workplaces, may have resulted in significantly higher cases reported. In addition, the low number of cases in provinces with higher poverty populations can be explained by the lack of access to testing opportunities, as argued in previous studies by (28,29).…”

Comparison of Conventional Modeling Techniques with the Neural Network Autoregressive Model (NNAR): Application to COVID-19 Data

“…As of late, there have been a handful of works that attempt to understand the spread of COVID-19 as well as predict confirmed cases and deaths of COVID-19, especially making use of statistical methodologies. For example, Kucharski et al [ 17 ] investigated a blend of stochastic transmission models on four data sets that caught the everyday number of new cases, the day-to-day number of new internationally sent out cases, the extent of contaminated travelers on departure flight, and the quantity of new confirmed cases to appraise the transmission elements of the illness throughout some time [ 18 ]. Machine learning-based model has been applied to analyze and predict the growth of COVID-19 [ 19 ].…”

Profile Analysis of COVID-19 Patients in Jambi Province

“…Kuta District, which has the highest density level in Bandung Regency, experienced a not too significant increase in cases. However, there is an increase in mild cases with local transmission from residents in the area [28].…”