Investigating Linkages Between Spatiotemporal Patterns of the COVID-19 Delta Variant and Public Health Interventions in Southeast Asia: Prospective Space-Time Scan Statistical Analysis Method

Abstract

Background The COVID-19 Delta variant has presented an unprecedented challenge to countries in Southeast Asia (SEA). Its transmission has shown spatial heterogeneity in SEA after countries have adopted different public health interventions during the process. Hence, it is crucial for public health authorities to discover potential linkages between epidemic progression and corresponding interventions such that collective and coordinated control measurements can be designed to increase their effectiveness at reducing transmission in SEA. Objective The purpose of this study is to explore potential linkages between the spatiotemporal progression of the COVID-19 Delta variant and nonpharmaceutical intervention (NPI) measures in SEA. We detected the space-time clusters of outbreaks of COVID-19 and analyzed how the NPI measures relate to the propagation of COVID-19. Methods We collected district-level daily new cases of COVID-19 from June 1 to October 31, 2021, and district-level population data in SEA. We adopted prospective space-time scan statistics to identify the space-time clusters. Using cumulative prospective space-time scan statistics, we further identified variations of relative risk (RR) across each district at a half-month interval and their potential public health intervention linkages. Results We found 7 high-risk clusters (clusters 1-7) of COVID-19 transmission in Malaysia, the Philippines, Thailand, Vietnam, and Indonesia between June and August, 2021, with an RR of 5.45 (P<.001), 3.50 (P<.001), 2.30 (P<.001), 1.36 (P<.001), 5.62 (P<.001), 2.38 (P<.001), 3.45 (P<.001), respectively. There were 34 provinces in Indonesia that have successfully mitigated the risk of COVID-19, with a decreasing range between –0.05 and –1.46 due to the assistance of continuous restrictions. However, 58.6% of districts in Malaysia, Singapore, Thailand, and the Philippines saw an increase in the infection risk, which is aligned with their loosened restrictions. Continuous strict interventions were effective in mitigating COVID-19, while relaxing restrictions may exacerbate the propagation risk of this epidemic. Conclusions The analyses of space-time clusters and RRs of districts benefit public health authorities with continuous surveillance of COVID-19 dynamics using real-time data. International coordination with more synchronized interventions amidst all SEA countries may play a key role in mitigating the progression of COVID-19.

Unraveling varying spatiotemporal patterns of dengue and associated exposure-response relationships with environmental variables in Southeast Asian countries before and during COVID-19

“…This situation may be exacerbated by the regional synchrony of dengue risk, highlighting the need for regional collaboration in dengue control (Servais, 2009). The SEA countries should work together to prevent, detect, and treat dengue, starting with sharing relevant data with each other (Luo et al, 2023, 2022). The detection of specific dengue serotypes in one country should be made known to other countries as soon as possible so that they can adopt more targeted prevention and detection measures to minimize the risk.…”

Long-term Association between NO2and Human Mobility: A Two-year Spatiotemporal Study during the COVID-19 Pandemic in Southeast Asia

“…Nevertheless, other meteorological factors especially temperature also had considerable impacts and should be considered along with mobility when attempting to understand NO 2 variation. Although SEA countries are gradually loosening movement and other restrictions to restore industry activities and economy (Luo et al ., 2022), traffic control is still a recommended solution for regions suffering from poor air quality. Additionally, our model provides an accurate NO 2 prediction method, which could be adopted as a health risk warning system in preventing possible exposure to high concentration of NO 2 .…”

“…Despite various mitigation measures implemented, such air pollutant has been a long-standing issue in Southeast Asia (SEA) (Kanniah et al ., 2020). During the COVID-19 pandemic, countries in SEA had imposed stringent lockdowns in 2020 along with various restriction measures (e.g., remote working, curfew, restrictions on social gathering and traveling) which limited human movement and activities to a great extent (Luo et al ., 2022). These changes in human behaviours provided an unprecedented opportunity to study how decrease in citizens’ mobility affects air pollution (Piccoli et al ., 2020).…”

A Spatiotemporal Solution to Control COVID-19 Transmission at the Community Scale for Returning to Normalcy: COVID-19 Symptom Onset Risk Spatiotemporal Analysis

“…It can further support effective epidemic control at the urban-community level for returning to normal. The Space-Time Scan Statistical Analysis Method [34], the improved Susceptible Exposure Infection and Recovery model [35][36][37], the spatiotemporal Bayesian inference model [38], and the hierarchical clustering analysis [39] were used in the previous studies to investigate the impact of the spatiotemporal spread of COVID-19. Consistent with the conclusions of these studies, results of this study also show that precise epidemic prevention and control measures can help effectively control the epidemic.…”