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

The enforcement of COVID-19 interventions by diverse governmental bodies, coupled with the indirect impact of COVID-19 on short-term environmental changes (e.g. plant shutdowns lead to lower greenhouse gas emissions), influences the dengue vector. This provides a unique opportunity to investigate the impact of COVID-19 on dengue transmission and generate insights to guide more targeted prevention measures. We aim to compare dengue transmission patterns and the exposure-response relationship of environmental variables and dengue incidence in the pre- and during-COVID-19 to identify variations and assess the impact of COVID-19 on dengue transmission. We initially visualized the overall trend of dengue transmission from 2012-2022, then conducted two quantitative analyses to compare dengue transmission pre-COVID-19 (2017-2019) and during-COVID-19 (2020-2022). These analyses included time series analysis to assess dengue seasonality, and a Distributed Lag Non-linear Model (DLNM) to quantify the exposure-response relationship between environmental variables and dengue incidence. We observed that all subregions in Thailand exhibited remarkable synchrony with a similar annual trend except 2021. Cyclic and seasonal patterns of dengue remained consistent pre- and during-COVID-19. Monthly dengue incidence in three countries varied significantly. Singapore witnessed a notable surge during-COVID-19, particularly from May to August, with cases multiplying several times compared to pre-COVID-19, while seasonality of Malaysia weakened. Exposure-response relationships of dengue and environmental variables show varying degrees of change, notably in Northern Thailand, where the peak relative risk for the maximum temperature-dengue relationship rose from about 3 to 17, and the max RR of overall cumulative association 0-3 months of relative humidity increased from around 5 to 55. Our study is the first to compare dengue transmission patterns and their relationship with environmental variables before and during COVID-19, showing that COVID-19 has affected dengue transmission at both the national and regional level, and has altered the exposure-response relationship between dengue and the environment.

Single-Cell RNA Sequencing Reveals Heterogeneity of ALI Model and Epithelial Cell Alterations after Exposure to Electronic Cigarette Vapor (preprint)

Background Electronic cigarettes (e-cigarettes) have been advertised as a healthier alternative to traditional cigarettes; however, their exact effects on the bronchial epithelium are poorly understood. Air-liquid interface (ALI) culture allows human primary bronchial epithelial cells to differentiate into bronchial epithelium (ALI-HBE), providing an in vitro model that simulates the biological characteristics of normal bronchial epithelium.Methods Single-cell RNA sequencing of ALI-HBE was used to reveal previously unrecognized transcriptional heterogeneity within the human bronchial epithelium and cell type–specific responses to acute exposure to e-cigarette vapor (e-vapor) containing distinct components (nicotine and/or flavoring).Results Acute exposure to e-vapor containing nicotine affected gene expression related to secretory function and basal-to-secretory transformation. In addition, acute exposure to e-vapor containing flavoring might promote susceptibility to virus infection and activate epithelial-to-mesenchymal transition.Conclusion The ALI-HBE model recapitulates the heterogeneity and transcriptional characteristics of human bronchial epithelium. Single-cell sequencing data provided high-resolution insights into e-vapor–induced remodeling of bronchial epithelium. The data also indicate factors on bronchial epithelial cells that may promote SARS-CoV-2 infection and suggest therapeutic targets for restoring normal bronchial epithelium function after e-cigarette use.

SARS-CoV-2 N protein-induced Dicer, XPO5, SRSF3, and hnRNPA3 downregulation causes pneumonia (preprint)

Age is a major risk factor for coronavirus disease (COVID-19)-associated severe pneumonia and mortality; however, the underlying mechanism remains unclear. Herein, we investigated whether age-related deregulation of RNAi components and RNA splicing factors affects COVID-19 severity. Decreased expression of RNAi components (Dicer and XPO5) and splicing factors (SRSF3 and hnRNPA3) correlated with increased severity of COVID-19 and SARS-CoV-2 nucleocapsid (N) protein-induced pneumonia. N protein induced autophagic degradation of Dicer, XPO5, SRSF3, and hnRNPA3, repressing miRNA biogenesis and RNA splicing and inducing DNA damage, proteotoxic stress, and pneumonia. Dicer, XPO5, SRSF3, and hnRNPA3 were downregulated with age in mouse lung tissues. Older mice experienced more severe N protein-induced pneumonia than younger mice. However, treatment with a poly(ADP-ribose) polymerase inhibitor (PJ34) or aromatase inhibitor (anastrozole) relieved N protein-induced pneumonia by restoring Dicer, XPO5, SRSF3, and hnRNPA3 expression. These findings will aid in developing improved treatments for SARS-CoV-2-associated pneumonia.

Dual Effects of the US-China Trade War and COVID-19 on United States Imports: Transfer of China's industrial chain? (preprint)

The trade tension between the U.S. and China since 2018 has caused a steady decoupling of the world's two largest economies. The pandemic outbreak in 2020 complicated this process and had numerous unanticipated repercussions. This paper investigates how U.S. importers reacted to the trade war and worldwide lockdowns due to the COVID-19 pandemic. We examine the effects of the two incidents on U.S. imports separately and collectively, with various economic scopes. Our findings uncover intricate trading dynamics among the U.S., China, and Southeast Asia, through which businesses relocated portions of their global supply chain away from China to avoid high tariffs. Our analysis indicates that increased tariffs cause the U.S. to import less from China. Meanwhile, Southeast Asian exporters have integrated more into value chains centered on Chinese suppliers by participating more in assembling and completing products. However, the worldwide lockdowns over pandemic have reversed this trend as, over this period, the U.S. effectively imported more goods directly from China and indirectly through Southeast Asian exporters that imported from China.

Spatiotemporal Variations of "Triple-demic" Outbreaks of Respiratory Infections in the United States in the Post-COVID-19 Era (preprint)

Objectives: The United States confronted a "triple-demic" of influenza, respiratory syncytial virus, and COVID-19 in the winter of 2022, resulting in increased respiratory infections and a higher demand for medical supplies. It is urgent to analyze each epidemic and their co-occurrence in space and time to identify hotspots and provide insights for public health strategy. Methods: We used retrospective space-time scan statistics to retrospect the situation of COVID-19, influenza, and RSV in 51 US states from October 2021 to February 2022, and then applied prospective space-time scan statistics to monitor spatiotemporal variations of each individual epidemic, respectively and collectively from October 2022 to February 2023. Results: Our analysis indicated that compared to the winter of 2021, COVID-19 cases decreased while influenza and RSV infections increased significantly during the winter of 2022. We revealed that a twin-demic high-risk cluster of influenza and COVID-19 but no triple-demic clusters emerged during the winter of 2021. We further identified a large high-risk cluster of triple-demic in the central US from late November, with COVID-19, influenza, and RSV having relative risks of 1.14, 1.90, and 1.59, respectively. The number of states at high risk for multiple-demic increased from 15 in October 2022 to 21 in January 2023. Conclusion: Our study provides a novel spatiotemporal perspective to explore and monitor the transmission patterns of the triple epidemic, which could inform public health authorities' resource allocation to mitigate future outbreaks.

AT2 Cell-Derived IgA Trapped by the Extracellular Matrix and Promoted Pulmonary Fibrosis (preprint)

Pulmonary fibrosis is an interstitial lung disease caused by various factors such as exposure to workplace environmental contaminants, drugs, or X-rays. Epithelial cells are among the driving factors of pulmonary fibrosis. Immunoglobulin A (IgA), traditionally thought to be secreted by B cells, is an important immune factor involved in COVID-19 infection and vaccination. In current study, we found lung epithelial cells were involved in IgA secretion which, in turn, promoted pulmonary fibrosis. The spatial transcriptomics and single-cell sequencing suggests that Igha transcripts were highly expressed in the fibrotic lesion areas of lungs from silica-treated mice. Reconstruction of B-cell receptor (BCR) sequences revealed a new cluster of AT2-like epithelial cells with a shared BCR and high expression of genes related to IgA production. Furthermore, the secretion of IgA by AT2-like cells were trapped by extracellular matrix and aggravated pulmonary fibrosis by activating fibroblasts. Targeted blockade of IgA secretion by pulmonary epithelial cells may be a potential strategy for treating pulmonary fibrosis.

Personal Protective Equipment Waste Management Behavior of Undergraduates in Xi'an City: Extended Theory of Value-Identity-Personal Norm Model (preprint)

The COVID-19 pandemic has changed people’s lives, with the most prominent change being the use of personal protective equipment (PPE). In this study, we used the extended Value-Identity-Personal (VIP) norm model to empirically analyze the influencing factors of pro-environmental behavior (PEB) among college students in Xi 'an, China, while considering the usage of PPE as an example of PEB. We proposed nine hypothetical questions, and the VIP model was established through the SmartPLS software to test the valid questionnaires of 414 college students. The verification results indicated that all the nine hypotheses were supported statistically, with personal environmental social responsibility and personal norms showing the most significant direct impact on PEB; notably, personal norms were also strongly influenced by environmental personal social responsibility. Biosphere values affected PEB indirectly, through self-identity and individual norms. This study proposes viable countermeasures and suggestions for college students to improve PEB; our findings can serve as a reference for policymakers and stakeholders to ensure the effective waste management of personal safety equipment.

Long-term Association between NO2 and Human Mobility: A Two-year Spatiotemporal Study during the COVID-19 Pandemic in Southeast Asia (preprint)

Since the COVID-19 pandemic, governments have implemented lockdowns and movement restrictions to contain the disease outbreak. Previous studies have reported a significant positive correlation between NO2 and mobility level during the lockdowns in early 2020. Though NO2 level and mobility exhibited similar spatial distribution, our initial exploration indicated that the decreased mobility level did not always result in concurrent decreasing NO2 level during a two-year time period in Southeast Asia with human movement data at a very high spatial resolution (i.e., Facebook origin-destination data). It indicated that factors other than mobility level contributed to NO2 level decline. Our subsequent analysis used a trained Multi-Layer Perceptron model to assess mobility and other contributing factors (e.g., travel modes, temperature, wind speed) and predicted future NO2 levels in Southeast Asia. The model results suggest that, while as expected mobility has a strong impact on NO2 level, a more accurate prediction requires considering different travel modes (i.e., driving and walking). Mobility shows two-sided impacts on NO2 level: mobility above the average level has a high impact on NO2, whereas mobility at a relatively low level shows negligible impact. The results also suggest that spatio-temporal heterogeneity and temperature also have impacts on NO2 and they should be incorporated to facilitate a more comprehensive understanding of the association between NO2 and mobility in the future study.

Content analysis of COVID-19 rumors

Objectives: This study provided a basis for the public to evaluate numerous sources of COVID-19 information to mitigate the harm caused by the repeated dissemination and sharing of misinformation. Methods: Content analysis was used to examine the spread of COVID-19 rumors on the Internet. The content characteristics and expressions of 113 COVID-19 rumors collected from the "Taiwan Fact Checking Center" website were used as samples for analysis. Results: The most common type of COVID-19 rumor was "aggressive," and "a particular behavior" and "specific type of food or appliance" were the most common objects of the rumors. The dates of occurrence were not clearly noted, but the contents of more than half of the rumors were accurately described. The main purposes were primarily "attention/warning" and "sharing new knowledge." These rumors mostly originated from the Internet, and the rumors were corroborated primarily by "photos/icons/videos" and "experts." Conclusions: The content analysis results of the COVID-19 rumors could enhance the public's basic awareness and ability to identify false COVID-19 rumors and to encourage social media users to be more vigilant against the spread of disinformation.

The Impact of COVID-19 on NO2 and PM2.5 Levels and Their Associations with Human Mobility Patterns in Singapore (preprint)

The decline in NO 2 and PM 2.5 pollutant levels were observed during COVID-19 around the world, especially during lockdowns. Previous studies explained such observed decline with the decrease in human mobility, whilst overlooking the meteorological changes (e.g., rainfall, wind speed) that could mediate air pollution level simultaneously. This pitfall could potentially lead to over-or under-estimation of the effect of COVID-19 on air pollution. Consequently, this study aims to re-evaluate the impact of COVID-19 on NO 2 and PM 2.5 pollutant level in Singapore, by incorporating the effect of meteorological parameters in predicting NO 2 and PM 2.5 baseline in 2020 using machine learning methods. The results found that NO 2 and PM 2.5 declined by a maximum of 38% and 36%, respectively, during lockdown period. As two proxies for change in human mobility, taxi availability and carpark availability were found to increase and decrease by a maximum of 12.6% and 9.8%, respectively, in 2020 from 2019 during lockdown. To investigate how human mobility influenced air pollutant level, two correlation analyses were conducted: one between PM 2.5 and carpark availability changes at regional scale and the other between NO 2 and taxi availability changes at a spatial resolution of 0.01°. The NO 2 variation was found to be more associated with the change in human mobility, with the correlation coefficients vary spatially across Singapore. A cluster of stronger correlations were found in the South and East Coast of Singapore. Contrarily, PM 2.5 and carpark availability had a weak correlation, which could be due to the limit of regional analyses. Drawing to the wider context, the high association between human mobility and NO 2 in the South and East Coast area can provide insights into future NO 2 reduction policy in Singapore. Graphical Abstract

Drive high power UVC-LED wafer into low-cost 4-inch era: effect of strain modulation (preprint)

Ultraviolet-C light-emitting diodes (UVC-LEDs) have great application in pathogen inactivation under various kinds of situations, especially in the fight against the COVID-19. Unfortunately, its epitaxial wafers are so far limited to 2-inch size, which greatly increases the cost of massive production. In this work, we report the 4-inch crack-free high-power UVC-LED wafer. This achievement relies on a proposed strain-tailored strategy, where a three-dimensional to two-dimensional (3D-2D) transition layer is introduced during the homo-epitaxy of AlN on high temperature annealed (HTA)-AlN template, which successfully drives the original compressive strain into tensile one and thus solves the challenge of realizing high quality Al$_{0.6}$Ga$_{0.4}$N layer with a flat surface. This smooth Al$_{0.6}$Ga$_{0.4}$N layer is nearly pseudomorphically grown on the strain-tailored HTA-AlN template, leading to 4-inch UVC-LED wafers with outstanding performances. Our strategy succeeds in compromising the bottlenecked contradictory in producing large-sized UVC-LED wafer on pronounced crystalline AlN template: The compressive strain in HTA-AlN allows for crack-free 4-inch wafer, but at the same time leads to a deterioration of the AlGaN morphology and crystal quality. The launch of 4-inch wafers makes the chip fabrication process of UVC-LEDs matches the mature blue one, and will definitely speed up the universal of UVC-LED in daily life.

A universal TLR7-nanoparticle adjuvant promotes broad immune responses against heterologous strains of Influenza and SARS-CoV-2 (preprint)

Fully effective vaccines for viruses such as Influenza and SARS-CoV-2 must elicit a diverse repertoire of antibodies against multiple drifted virus strains. However, how to achieve a diverse response has no general solution except to combine multiple strains, which risks diluting the response for all strains included. Here, we describe the synthesis of a universal, toll-like receptor 7 agonist (TLR7)-nanoparticle adjuvant, TLR7-NP, constructed from TLR7 agonist-initiated ring-opening polymerization of lactide and self-assembly with poly(ethylene glycol)- b -poly(lactic-co-glycolic acid). When mixed with Alum-adsorbed antigens, this TLR7-NP adjuvant elicited cross-reactive antibodies for both dominant and subdominant epitopes, as well as antigen-specific CD8 + T cell responses. TLR7-NPs adjuvanted influenza subunit vaccine successfully protected mice from heterologous viral challenge. TLR7-NPs also enhanced the antibody response to a SARS-CoV-2 subunit vaccine against multiple variants and revealed the mobilization of a virus-like response. We further demonstrate enhanced antigen-specific responses in human tonsil organoids with this novel adjuvant.

Revealing public opinion towards COVID-19 vaccines using Twitter data in the United States: a spatiotemporal perspective (preprint)

Background: The COVID-19 pandemic has imposed a large, initially uncontrollable, public health crisis both in the United States (US) and across the world, with experts looking to vaccines as the ultimate mechanism of defense. The development and deployment of COVID-19 vaccines have been rapidly advancing via global efforts. Hence, it is crucial for governments, public health officials, and policy makers to understand public attitudes and opinions towards vaccines, such that effective interventions and educational campaigns can be designed to promote vaccine acceptance. Objective: The aim of this study is to investigate public opinion and perception on COVID-19 vaccines by investigating the spatiotemporal trends of their sentiment and emotion towards vaccines, as well as how such trends relate to popular topics on Twitter in the US. Methods: We collected over 300,000 geotagged tweets in the US from March 1, 2020 to February 28, 2021. We examined the spatiotemporal patterns of public sentiment and emotion over time at both national and state scales and identified three phases along the pandemic timeline with the significant changes of public sentiment and emotion. We further linked the changes to eleven key events and major topics as the potential drivers to induce such changes via cloud mapping of keywords and topic modeling. Results: An increasing trend of positive sentiment in parallel with the decrease of negative sentiment are generally observed in most states, reflecting the rising confidence and anticipation of the public towards COVID-19 vaccines. The overall tendency of the eight types of emotion implies the trustiness and anticipation of the public to vaccination, accompanied by the mixture of fear, sadness and anger. Critical social/international events and/or the announcements of political leaders and authorities may have potential impacts on the public opinion on vaccines. These factors, along with important topics and manual reading of popular posts on eleven key events, help identify underlying themes and validate insights from the analysis. Conclusions: The analyses of near real-time social media big data benefit public health authorities by enabling them to monitor public attitudes and opinions towards vaccine-related information in a geo-aware manner, address the concerns of vaccine skeptics and promote the confidence of individuals within a certain region or community, towards vaccines.

UNDERESTIMATION OF NOVEL RISKS AND ANTI-PANDEMIC PERFORMANCE: THE MODERATING EFFECTS OF POLITICS AND ADAPTIVE INNOVATION

Based on prior research and observations of global responses to the COVID-19 pandemic, we study the relationship between underestimation of novel risks and the performance of fighting adversities (PFA). While prior research suggests that underestimation of risks may have some positive effects on PFA, we propose that, for novel adversities such as the COVID-19 pandemic, underestimation of its risk can be very harmful and damaging. We also propose that the relationship between the underestimation and PFA can be moderated by intensiveness of politics (IPS) and adaptive innovation. The contingent model in this paper provides insightful practical implications to risk and disaster management in human collectives.

The effects of different travel modes on COVID-19 transmission in global cities (preprint)

Previous studies have been focused primarily on modelling and predicting the transmission of COVID-19. While little research has been conducted to understand the impacts of different travel modes on the transmission of COVID-19, without an explicit understanding of the travel mode effects, many people intuitively perceive non-motorized travel modes to be safer than public transit as passengers in public transit are confined to small, enclosed spaces where the virus can transmit more easily. During the period when urban mobility gradually returns towards what was called ‘normal’ and transit systems and urban facilities reopen, new waves of the pandemic might be generated as travel mode choices significantly differ across cities and different travel behaviors are associated with diverse infectious sources. Thus, the current study focuses on understanding the impact of different travel modes on the transmission of COVID-19 in the long-term and at world-wide scales, aspects that have not received much attention in the research literature. Accordingly, a multivariate time series analysis has been developed to examine the impacts of daily confirmed cases and travel modes, based on driving, public transit, and walking as recorded in the Apple Mobility Trends Reports on COVID-19 transmission risks in 71 cities throughout the world from January to November 2020. The impact of population density in built-up areas and the degree to which the `wearing' of facemasks affects infections are also investigated. Among the three travel modes we examine, driving is the safest way to commute because drivers are physically separate from crowds. Unexpectedly, walking has a relatively low risk when the population density in built-up areas is high, which suggests that, globally, people have increased awareness of pandemic prevention. Although the general public is more worried about using public transit, this mode can still be safe in many large cities, a factor that is vital for informing policy making and developing trust among citizens so they will continue to commute using public transit when strict preventative measures are in place. From another perspective, infectious sources make the largest contribution to daily confirmed cases, thus demonstrating the importance of strict quarantine measures to block the source of infection. The results and conclusions presented herein are based on an analysis of spatio-temporal data that helps inform policy making and enable cities to be kept open when controlling the pandemic, which has become an urgent task for the international community when rebuilding the economy.

Effectiveness of Contact Tracing, Mask Wearing and Prompt Testing on Suppressing COVID-19 Resurgences in Megacities: An Individual-Based Modelling Study (preprint)

Background: Secondary waves of COVID-19 loom in many countries as strict physical distancing measures have been lifted. Since megacities have been hardest hit by the disease, science-based guidelines of non-pharmaceutical interventions are still in need for post-epidemic management in ‘business as usual’ cities before vaccines are widely available. This study aims to investigate the combined effects of contact tracing, mask wearing, and prompt testing on minimizing the risk of next COVID-19 waves in megacities. Methods: We integrated 5·8 million mobile phone users’ trajectory records into a spatially explicit individual-based model for simulating COVID-19 spread among 4·5 million households, 230 thousand workplaces (including schools), and other public places in 0.6 million buildings in Shenzhen city, China, which has been gradually reopened. Government interventions were incorporated to reconstruct the actual course of the 1st wave epidemic. After validated by empirical data, the model was used to assess the probability of resurgences if sporadic cases occurred in a fully reopened city under different scenarios of contact tracing settings (household, work, school, and public place), mask use, and test-seeking behavior along with receding public vigilance. Findings: Our model well predicted the spatiotemporal dynamics of the 1st wave epidemic in Shenzhen, by age distribution of symptomatic cases, and household secondary attack rate (11·02%). After city reopens, our results show a 50% chance or less of suppressing disease resurgence if not implementing contact tracing. Tracing household contacts, in combination with mandatory (100% compliance) mask use and prompt testing could limit the probability of next outbreak under 5%. If contact tracing can be expanded to work/class group members, the public compliance of masking and testing can be relaxed to 80% and 40%, respectively, to achieve the same suppression target. Further scaled-up contact tracing that includes casual contacts can suppress resurgences with a low compliance to mask use (40%) and prompt testing (20%-40%). Interpretation: To minimize the risk of resurgence in a reopened city, the local government is expected to spare no efforts to trace close contacts in household, workplace and school for a confirmed case. The authorities should promote mask use in a public space and encourage people with COVID-19-like symptoms to testing within two days after illness onset, along with measures such as sick leave compensation and extensive temperature screening in public places.Funding Statement: National Scientific Foundation of China, R & D project of key areas in Guangdong Province, Bill & Melinda Gates Foundation, Joint Engineering Research Center for Health Big Data Intelligent Analysis TechnologyDeclaration of Interests: We declare no competing interests.

Effectiveness of Contact Tracing, Mask Wearing and Prompt Testing on Suppressing COVID-19 Resurgences in Megacities: An Individual-Based Modelling Study (preprint)

Background: As COVID-19 resurges in many countries, science-based guidelines of non-pharmaceutical interventions are still in need for post-epidemic management in ‘business as usual’ cities before vaccines are widely available. This study aims to investigate the combined effects of contact tracing, mask wearing, and prompt testing on minimizing the risk of next COVID-19 waves caused by sporadic outbreaks in megacities.Methods: We integrated large-scale mobile phone tracking data into a spatially explicit individual-based model to simulate COVID-19 spread among 11.2 million individuals in Shenzhen City, China. Government interventions were incorporated to reconstruct the actual course of the 1st wave epidemic. After validated by empirical data, the model was used to assess the probability of resurgences if sporadic cases occurred in a fully reopened city under different scenarios of contact tracing settings (household, work, school, and public place), mask use, and test-seeking behavior along with receding public vigilance.Results: Our model well predicted the spatiotemporal dynamics of the 1st wave epidemic in Shenzhen, by age distribution of symptomatic cases, and household secondary attack rate (11·02%). After city reopens, our results show a 50% chance or less of suppressing disease resurgence if not implementing contact tracing. Tracing household contacts, in combination with mandatory (100% compliance) mask use and prompt testing could limit the probability of next outbreak under 5%. If contact tracing can be expanded to work/class group members, the public compliance of masking and testing can be relaxed to 80% and 40%, respectively, to achieve the same suppression target. Further scaled-up contact tracing that includes casual contacts can suppress resurgences with a low compliance to mask use (40%) and prompt testing (20%-40%). Conclusions: To minimize the risk of resurgence in a reopened city, the local government is expected to spare no efforts to trace close contacts in household, workplace and school for a confirmed case. The authorities should promote mask use in a public space and encourage people with COVID-19-like symptoms to testing within two days after illness onset, along with measures such as sick leave compensation and extensive temperature screening in public places.

Structure-based drug designing and immunoinformatics approach for SARS-CoV-2

The article offers information about the structure-based drug designing and immunoinformatics approach for SARS-CoV-2. It discusses the prevalence of respiratory illness caused by the novel SARS-CoV-2 virus associated with multiple organ failures is spreading rapidly because of its contagious human-to-human transmission and inadequate global health care systems.

Space-time patterns, change, and propagation of COVID-19 risk relative to the intervention scenarios in Bangladesh (preprint)

The novel coronavirus (COVID-19) pandemic continues to be a significant public health threat worldwide. As of mid-June 2020, COVID-19 has spread worldwide with more than 7.7 million confirmed cases and more than 400,000 deaths. The impacts are substantial particularly in developing and densely populated countries like Bangladesh with inadequate health care facilities, where COVID-19 cases are currently surging. While early detection and isolation were identified as important non-pharmaceutical intervention (NPI) measures for containing the disease spread, this may not be pragmatically implementable in developing countries primarily due to social and economic reasons (i.e. poor education, less public awareness, massive unemployment). To shed light on COVID-19 transmission dynamics and impacts of NPI scenarios, e.g. social distancing, this study conducted emerging pattern analysis using the space-time scan statistic at district and thana (i.e. a sub-district or 'upazila' with at least one police station) levels in Bangladesh and its capital Dhaka city, respectively. We found that the central and south eastern regions in Bangladesh are currently exhibiting a high risk of COVID-19 transmission. Dhaka megacity remains as the highest risk "active" cluster since early April. The space-time progression of COVID-19 infection, when validated against the chronicle of government press releases and newspaper reports, suggests that Bangladesh have experienced a community level transmission at the early phase (i.e., March, 2020) primarily introduced by Bangladeshi citizens returning from coronavirus-affected countries in the Europe and the Middle East. A linkage is evident between the violation of NPIs and post-incubation period emergence of new clusters with elevated exposure risk around Bangladesh. This study provides novel insights into the space-time patterns of COVID-19 transmission dynamics and recommends pragmatic NPI implementation for reducing disease transmission and minimizing impacts in a resource-scarce country with Bangladesh as a case-study example.