Integrating local and neighboring area influences into vulnerability modeling of infectious diseases in Singapore

Infectious disease spreading is a spatial interaction process. Assessing community vulnerability to infectious diseases thus requires not only information on local demographic and built environmental conditions, but also insights into human activity interactions with neighboring areas that can lead to the transition of vulnerability from locations to locations. This study presented an analytical framework based on the Particle Swarm Optimization model to estimate the weights of the factors for vulnerability modeling, and a local proportional parameter for use in the integration of the local and neighboring area risks. A country model and five cross-region validation models were developed for the case study of Singapore to assess the vulnerability to COVID-19. The results showed that the identified weights for the factors were robust throughout the optimization process and across various models. The local proportional parameter could be set slightly higher in between 0.6 and 0.8 (out of 1), signifying that the local effect was higher than the neighboring effect. Computation of the weights from the optimal solutions for the integrated vulnerability index showed that the factors of human activity intensity and accessibility to amenities had much higher weights, at 0.5 and 0.3, respectively. Conversely, the weights of population density, elderly population, social economic status and land use diversity were much lower. These findings underscored the importance of considering non-equal weights for factors and incorporating spatial interactions between local and neighboring areas for vulnerability modeling, to provide to a more comprehensive assessment of vulnerability to infectious diseases.

COVID-19 as a Trigger for type 1 diabetes.

Type 1 diabetes (T1D) is usually caused by immune-mediated destruction of islet beta-cells, and genetic and environmental factors are thought to trigger autoimmunity. Convincing evidence indicates that viruses are associated with T1D development and progression. During the coronavirus disease 2019(COVID-19) pandemic, cases of hyperglycemia, diabetic ketoacidosis (DKA), and new diabetes increased, suggesting that severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) may be a trigger for or unmask T1D. Possible mechanisms of beta-cell damage include virus-triggered cell death, immune-mediated loss of pancreatic beta-cells, and damage to beta-cells due to infection of surrounding cells. This article examines the potential pathways by which SARS-CoV-2 affects islet beta-cells in the above three aspects. Specifically, we emphasize that T1D can be triggered by SARS-CoV-2 through several autoimmune mechanisms, including epitope spread, molecular mimicry and bystander activation. Given that the development of T1D is often a chronic, long-term process, it is difficult to currently draw firm conclusions as to whether SARS-CoV-2 causes T1D. This area needs to be focused on in terms of the long-term outcomes. More in-depth and comprehensive studies with larger cohorts of patients and long-term clinical follow-ups are required.

Smoking modulates different secretory subpopulations expressing SARS-CoV-2 entry genes in the nasal and bronchial airways.

SARS-CoV-2 infection and disease severity are influenced by viral entry (VE) gene expression patterns in the airway epithelium. The similarities and differences of VE gene expression (ACE2, TMPRSS2, and CTSL) across nasal and bronchial compartments have not been fully characterized using matched samples from large cohorts. Gene expression data from 793 nasal and 1673 bronchial brushes obtained from individuals participating in lung cancer screening or diagnostic workup revealed that smoking status (current versus former) was the only clinical factor significantly and reproducibly associated with VE gene expression. The expression of ACE2 and TMPRSS2 was higher in smokers in the bronchus but not in the nose. scRNA-seq of nasal brushings indicated that ACE2 co-expressed genes were highly expressed in club and C15orf48+ secretory cells while TMPRSS2 co-expressed genes were highly expressed in keratinizing epithelial cells. In contrast, these ACE2 and TMPRSS2 modules were highly expressed in goblet cells in scRNA-seq from bronchial brushings. Cell-type deconvolution of the gene expression data confirmed that smoking increased the abundance of several secretory cell populations in the bronchus, but only goblet cells in the nose. The association of ACE2 and TMPRSS2 with smoking in the bronchus is due to their high expression in goblet cells which increase in abundance in current smoker airways. In contrast, in the nose, these genes are not predominantly expressed in cell populations modulated by smoking. In individuals with elevated lung cancer risk, smoking-induced VE gene expression changes in the nose likely have minimal impact on SARS-CoV-2 infection, but in the bronchus, smoking may lead to higher viral loads and more severe disease.

Impact of urbanisation and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore.

Geographical weighted regression (GWR) can be used to explore the COVID-19 transmission pattern between cases. This study aimed to explore the influence from environmental and urbanisation factors, and the spatial relationship between epidemiologically-linked, unlinked and imported cases during the early phase of the epidemic in Singapore. Spatial relationships were evaluated with GWR modelling. Community COVID-19 cases with residential location reported from 21st January 2020 till 17th March 2020 were considered for analyses. Temperature, relative humidity, population density and urbanisation are the variables used as exploratory variables for analysis. ArcGIS was used to process the data and perform geospatial analyses. During the early phase of COVID-19 epidemic in Singapore, significant but weak correlation of temperature with COVID-19 incidence (significance 0.5-1.5) was observed in several sub-zones of Singapore. Correlations between humidity and incidence could not be established. Across sub-zones, high residential population density and high levels of urbanisation were associated with COVID-19 incidence. The incidence of COVID-19 case types (linked, unlinked and imported) within sub-zones varied differently, especially those in the western and north-eastern regions of Singapore. Areas with both high residential population density and high levels of urbanisation are potential risk factors for COVID-19 transmission. These findings provide further insights for directing appropriate resources to enhance infection prevention and control strategies to contain COVID-19 transmission.

Differential health and economic impacts from the COVID-19 lockdown between the developed and developing countries: Perspective on air pollution.

It is enlightening to determine the discrepancies and potential reasons for the degree of impact from the COVID-19 control measures on air quality as well as the associated health and economic impacts. Analysis of air quality, socio-economic factors, and meteorological data from 447 cities in 46 countries indicated that the COVID-19 control measures had significant impacts on the PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm) concentrations in 20 (reduced PM2.5 concentrations of -7.4-29.1 µg m-3) of the selected 46 countries. In these 20 countries, the robustly distinguished changes in the PM2.5 concentrations caused by the control measures differed between the developed (95% confidence interval (CI): -2.7-5.5 µg m-3) and developing countries (95% CI: 8.3-23.2 µg m-3). As a result, the COVID-19 lockdown reduced death and hospital admissions change from the decreased PM2.5 concentrations by 7909 and 82,025 cases in the 12 developing countries, and by 78 and 1214 cases in the eight developed countries. The COVID-19 lockdown reduced the economic cost from the PM2.5 related health burden by 54.0 million dollars in the 12 developing countries and by 8.3 million dollars in the eight developed countries. The disparity was related to the different chemical compositions of PM2.5. In particular, the concentrations of primary PM2.5 (e.g., BC) in cities of developing countries were 3-45 times higher than those in developed countries, so the mass concentration of PM2.5 was more sensitive to the reduced local emissions in developing countries during the COVID-19 control period. The mass fractions of secondary PM2.5 in developed countries were generally higher than those in developing countries. As a result, these countries were more sensitive to the secondary atmospheric processing that may have been enhanced due to reduced local emissions.

Diffuse Alveolar Hemorrhage With Avelumab Maintenance Therapy.

Immune checkpoint blockade is a rapidly expanding therapeutic modality in oncology. However, its adverse effects extend beyond the cytotoxicity of conventional chemotherapy. Pneumotoxicity associated with immune checkpoint therapy presents a diagnostic conundrum that has been further complicated by the COVID-19 pandemic. We report a case of a patient with metastatic urothelial carcinoma who developed diffuse alveolar hemorrhage (DAH) following treatment with avelumab.

An Atypical Presentation of Acute Pulmonary Embolism With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Pneumonia.

Clinical presentation and severity of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) varies greatly amongst patients, as supported by recent literature. This poses an ongoing challenge in the diagnostic and therapeutic approach for managing these patients. Here, we would like to describe a case of acute bilateral pulmonary embolism (PE) presenting with atypical gastrointestinal symptoms in a patient with SARS-CoV-2 infection. This atypical presentation of PE is unique to our case and highlights the significance of a high index of clinical suspicion for SARS-CoV-2 and its associated thrombogenic effect, even in patients with atypical symptoms.

Changes in air quality related to the control of coronavirus in China: Implications for traffic and industrial emissions.

Measures taken to control the disease (Covid-19) caused by the novel coronavirus dramatically reduced the number of vehicles on the road and diminished factory production. For this study, changes in the air quality index (AQI) and the concentrations of six air pollutants (PM2.5, PM10, CO, SO2, NO2, and O3) were evaluated during the Covid-19 control period in northern China. Overall, the air quality improved, most likely due to reduced emissions from the transportation and secondary industrial sectors. Specifically, the transportation sector was linked to the NO2 emission reductions, while lower emissions from secondary industries were the major cause for the reductions of PM2.5 and CO. The reduction in SO2 concentrations was only linked to the industrial sector. However, the reductions in emissions did not fully eliminate air pollution, and O3 actually increased, possibly because lower fine particle loadings led to less scavenging of HO2 and as a result greater O3 production. These results also highlight need to control emissions from the residential sector.