The effect of public health interventions on COVID-19 incidence in Queensland, Australia: a spatial cluster analysis.

BACKGROUND: Using SaTScan™ Geographical Information Systems (GIS), spatial cluster analysis was used to examine spatial trends and identify high-risk clusters of Coronavirus 2019 (COVID-19) incidence in response to changing levels of public health intervention phases including international and state border closures, statewide vaccination coverage, and masking requirements. METHODS: Changes in COVID-19 incidence were mapped at the statistical area 2 (SA2) level using a GIS and spatial cluster analysis was performed using SaTScan™ to identify most-likely clusters (MLCs) during intervention phases. RESULTS: Over the study period, significant high-risk clusters were identified in Brisbane city (relative risk = 30.83), the southeast region (RR = 1.71) and moving to Far North Queensland (FNQ) (RR = 2.64). For masking levels, cluster locations were similar, with MLC in phase 1 in the southeast region (RR = 2.56) spreading to FNQ in phase 2 (RR = 2.22) and phase 3 (RR = 2.64). All p values <.0001. CONCLUSIONS: Movement restrictions in the form of state and international border closures were highly effective in delaying the introduction of COVID-19 into Queensland, with very low levels of transmission prior to border reopening while mandatory masking may have played a role in decreasing transmission through behavioural changes. Early clusters were in highly populated regions, as restrictions eased clusters were identified in regions more likely to be rural or remote, with higher numbers of Indigenous people, lower vaccination coverage or lower socioeconomic status.

Impacts of heatwaves on type 2 diabetes mortality in China: a comparative analysis between coastal and inland cities.

The impacts of extreme temperatures on diabetes have been explored in previous studies. However, it is unknown whether the impacts of heatwaves appear variations between inland and coastal regions. This study aims to quantify the associations between heat exposure and type 2 diabetes mellitus (T2DM) deaths in two cities with different climate features in Shandong Province, China. We used a case-crossover design by quasi-Poisson generalized additive regression with a distributed lag model with lag 2 weeks, controlling for relative humidity, the concentration of air pollution particles with a diameter of 2.5 µm or less (PM2.5), and seasonality. The wet- bulb temperature (Tw) was used to measure the heat stress of the heatwaves. A significant association between heatwaves and T2DM deaths was only found in the coastal city (Qingdao) at the lag of 2 weeks at the lowest Tw = 14℃ (relative risk (RR) = 1.49, 95% confidence interval (CI): 1.11-2.02; women: RR = 1.51, 95% CI: 1.02-2.24; elderly: RR = 1.50, 95% CI: 1.08-2.09). The lag-specific effects were significant associated with Tw at lag of 1 week at the lowest Tw = 14℃ (RR = 1.14, 95% CI: 1.03-1.26; women: RR = 1.15, 95% CI: 1.01-1.31; elderly: RR = 1.15, 95% CI: 1.03-1.28). However, no significant association was found in Jian city. The research suggested that Tw was significantly associated with T2DM mortality in the coastal city during heatwaves on T2DM mortality. Future strategies should be implemented with considering socio-environmental contexts in regions.

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations.

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

The Characteristics of the Influenza Virus Epidemic Around the SARS-CoV-2 Epidemic Period in the Pudong New Area of Shanghai.

OBJECTIVES: The concurrent impact of COVID-19 and influenza on disease burden is a topic of great concern. This discussion delves into the epidemiological characteristics of seasonal influenza activity in Shanghai within the context of the SARS-CoV-2 epidemic. METHODS: From 2017 to 2023, a total of 11,081 patients having influenza-like illness (ILI) were included in this study for influenza virus detection. Reverse transcription polymerase chain reaction (RT-PCR) assays were conducted according to standardised protocols to identify the types and subtypes of influenza viruses. The positivity rate of the influenza virus among the sampled ILI cases served as a surrogate measure for estimating various influenza seasonal characteristics, such as periodicity, duration, peak occurrences, and the prevalent subtypes or lineages. Epidemiological aspects across different years and age groups were subjected to comprehensive analysis. For categorical variables, the Chi-square test or Fisher's exact test was employed, as deemed appropriate. RESULTS: A total of 1553 (14.0%) tested positive for influenza virus pathogens. The highest positivity rate for influenza was observed in adults aged 25-59 years (18.8%), while the lowest rate was recorded in children under 5 years (3.8%). The influenza circulation patterns in Shanghai were characterised: (1) 2 years exhibited semiannual periodicity (2017-2018, 2022-2023); (2) 3 years displayed annual periodicity (2018-2019, 2019-2020, and 2021-2022); and (3) during 2020-2021, epidemic periodicities of seasonal influenza viruses disappeared. In terms of influenza subtypes, four subtypes were identified during 2017-2018. In 2018-2019 and 2019-2020, A/H3N2, A/H1N1, and B/Victoria were circulating. Notably, one case of B/Victoria was detected in 2020-2021. The epidemic period of 2021-2022 was attributed to B/Victoria, and during 2022-2023, the influenza A virus was the dominant circulating strain. CONCLUSIONS: The seasonal epidemic period and the predominant subtype/lineage of influenza viruses around the SARS-CoV-2 epidemic period in Shanghai city are complex. This underscores the necessity for vigilant influenza control strategies amidst the backdrop of other respiratory virus pandemics.

Epidemiological features of seasonal influenza transmission among 11 climate zones in Chinese Mainland.

BACKGROUND: Previous studies provided some evidence of meteorological factors influence seasonal influenza transmission patterns varying across regions and latitudes. However, research on seasonal influenza activities based on climate zones are still in lack. This study aims to utilize the ecological-based Köppen Geiger climate zones classification system to compare the spatial and temporal epidemiological characteristics of seasonal influenza in Chinese Mainland and assess the feasibility of developing an early warning system. METHODS: Weekly influenza cases number from 2014 to 2019 at the county and city level were sourced from China National Notifiable Infectious Disease Report Information System. Epidemic temporal indices, time series seasonality decomposition, spatial modelling theories including Moran's I and local indicators of spatial association were applied to identify the spatial and temporal patterns of influenza transmission. RESULTS: All climate zones had peaks in Winter-Spring season. Arid, desert, cold (BWk) showed up the first peak. Only Tropical, savannah (Aw) and Temperate, dry winter with hot summer (Cwa) zones had unique summer peak. Temperate, no dry season and hot summer (Cfa) zone had highest average incidence rate (IR) at 1.047/100,000. The Global Moran's I showed that average IR had significant clustered trend (z = 53.69, P < 0.001), with local Moran's I identified high-high cluster in Cfa and Cwa. IR differed among three age groups between climate zones (0-14 years old: F = 26.80, P < 0.001; 15-64 years old: F = 25.04, P < 0.001; Above 65 years old: F = 5.27, P < 0.001). Age group 0-14 years had highest average IR in Cwa and Cfa (IR = 6.23 and 6.21) with unique dual peaks in winter and spring season showed by seasonality decomposition. CONCLUSIONS: Seasonal influenza exhibited distinct spatial and temporal patterns in different climate zones. Seasonal influenza primarily emerged in BWk, subsequently in Cfa and Cwa. Cfa, Cwa and BSk pose high risk for seasonal influenza epidemics. The research finds will provide scientific evidence for developing seasonal influenza early warning system based on climate zones.

Constructing bodily emotion maps based on high-density body surface potentials for psychophysiological computing.

Emotion is a complex physiological and psychological activity, accompanied by subjective physiological sensations and objective physiological changes. The body sensation map describes the changes in body sensation associated with emotion in a topographic manner, but it relies on subjective evaluations from participants. Physiological signals are a more reliable measure of emotion, but most research focuses on the central nervous system, neglecting the importance of the peripheral nervous system. In this study, a body surface potential mapping (BSPM) system was constructed, and an experiment was designed to induce emotions and obtain high-density body surface potential information under negative and non-negative emotions. Then, by constructing and analyzing the functional connectivity network of BSPs, the high-density electrophysiological characteristics are obtained and visualized as bodily emotion maps. The results showed that the functional connectivity network of BSPs under negative emotions had denser connections, and emotion maps based on local clustering coefficient (LCC) are consistent with BSMs under negative emotions. in addition, our features can classify negative and non-negative emotions with the highest classification accuracy of 80.77%. In conclusion, this study constructs an emotion map based on high-density BSPs, which offers a novel approach to psychophysiological computing.

Climate change, environmental extremes, and human health in Australia: challenges, adaptation strategies, and policy gaps.

Climate change presents a major public health concern in Australia, marked by unprecedented wildfires, heatwaves, floods, droughts, and the spread of climate-sensitive infectious diseases. Despite these challenges, Australia's response to the climate crisis has been inadequate and subject to change by politics, public sentiment, and global developments. This study illustrates the spatiotemporal patterns of selected climate-related environmental extremes (heatwaves, wildfires, floods, and droughts) across Australia during the past two decades, and summarizes climate adaptation measures and actions that have been taken by the national, state/territory, and local governments. Our findings reveal significant impacts of climate-related environmental extremes on the health and well-being of Australians. While governments have implemented various adaptation strategies, these plans must be further developed to yield concrete actions. Moreover, Indigenous Australians should not be left out in these adaptation efforts. A collaborative, comprehensive approach involving all levels of government is urgently needed to prevent, mitigate, and adapt to the health impacts of climate change.

Comparative Analysis of Vaccine Inequity and COVID-19 Transmission Amid the Omicron Variant Among Countries - Six Countries, Asia-Pacific Region, 2022.

What is already known about this topic?: The coronavirus disease 2019 (COVID-19) persists as a significant global public health crisis. The predominant strain, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), notably the Omicron variant, continues to undergo mutations. While vaccination is heralded as the paramount solution to cease the pandemic, challenges persist in providing equitable access to COVID-19 vaccines. What is added by this report?: The distribution of vaccine coverage exhibited disparities between high-income and middle-income countries, with middle-income countries evidencing lower levels of vaccination. The data further suggested that countries with lesser vaccination levels tended to display a higher case fatality rate. Findings indicated that an increase in population-wide vaccination was effective in mitigating COVID-19 related mortalities. What are the implications for public health practice?: The findings of this research underscore the pressing necessity for equitable access to vaccines to effectively mitigate the COVID-19 pandemic within the Asia-Pacific region.

Impact of environmental factors on diabetes mortality: A comparison between inland and coastal areas.

BACKGROUND: Diabetes mortality varies between coastal and inland areas in Shandong Province, China. However, evidence about the reasons for this disparity is limited. We assume that distinct environmental conditions may contribute to the disparities in diabetes mortality patterns between coastal and inland areas. METHOD: Qingdao and Jinan were selected as typical coastal and inland cities in Shandong Province, respectively, with similar socioeconomic but different environmental characteristics. Data on diabetes deaths and environmental factors (i.e., temperature, relative humidity and air pollution particles with a diameter of 2.5 µm or less (PM2.5)) were collected from 2013 to 2020. Spatial kriging methods were used to estimate the aggregated diabetes mortality at the city level. A distributed lag non-linear model (DLNM) was used to quantify the possible cumulative and non-cumulative associations between environmental factors and diabetes mortality by age, sex and location. RESULTS: In the coastal city (Qingdao), the maximum cumulative relative risks (RRs) of temperature and PM2.5 associated with diabetes deaths were 2.54 (95 % confidence interval (CI): 1.25-5.15), and 1.17 (95 % CI: 1.01-1.37) respectively, at lag 1 week. In the inland city (Jinan), only temperature exhibited significant cumulative associations with diabetes deaths (RR = 1.54, 95 % CI: 1.07-2.23 at 29 °C). Lower relative humidity (22 %-45 %) had a lag-specific association with diabetes deaths in inland areas at lag 3 weeks (RR = 1.33, 95 % CI: 1.03-1.70 at 22 %). CONCLUSION: Despite the lower PM2.5 concentrations in the coastal location, diabetes mortality exhibited stronger links to environmental variables in the coastal city than in the inland city. These findings suggest that the control of air pollution could decrease the mortality burden of diabetes, even in the region with relatively good air quality. Additionally, the spatial estimation method is recommended to identify associations between environmental factors and diseases in studies with limited data.

Impact of climate factors and climate-gene interaction on systemic lupus erythematosus patients' response to glucocorticoids therapy.

BACKGROUND: Glucocorticoids (GCs) were the essential drugs for systemic lupus erythematosus (SLE). However, different patients differ substantially in their response to GCs treatment. Our current study aims at investigating whether climate variability and climate-gene interaction influence SLE patients' response to the therapy of GCs. METHODS: In total, 778 SLE patients received therapy of GCs for a study of 12-week follow-up. The efficacy of GCs treatment was evaluated using the Systemic Lupus Erythematosus Disease Activity Index. The climatic data were provided by China Meteorological Data Service Center. Additive and multiplicative interactions were examined. RESULTS: Compared with patients with autumn onset, the efficacy of GCs in patients with winter onset is relatively poor (ORadj = 1.805, 95%CIadj : 1.181-3.014, padj = 0.020). High mean relative humidity during treatment decreased the efficacy of GCs (ORadj = 1.033, 95%CIadj : 1.008-1.058, padj = 0.011), especially in female (ORadj = 1.039, 95%CIadj : 1.012-1.067, padj = 0.004). There was a significant interaction between sunshine during treatment and TRAP1 gene rs12597773 on GCs efficacy (Recessive model: AP = 0.770). No evidence of significant interaction was found between climate factors and the GR gene polymorphism on the improved GCs efficacy in the additive model. Multiplicative interaction was found between humidity in the month prior to treatment and GR gene rs4912905 on GCs efficacy (Dominant model: OR = 0.470, 95%CI: 0.244-0.905, p = 0.024). CONCLUSIONS: Our findings suggest that climate variability influences SLE patients' response to the therapy of GCs. Interactions between climate and TRAP1/GR gene polymorphisms were related to GCs efficacy. The results guide the individualized treatment of SLE patients.

Japanese encephalitis transmission trends in Gansu, China: A time series predictive model based on spatial dispersion.

Objective: This study serves to ascertain trends of space and time for Japanese encephalitis (JE) transmission at the township-level and develop an innovative time series predictive model to predict the geographical spread of JE in Gansu Province, China. Methods: We collected weekly data on JE from 2005 to 2019 at the township-level. Kriging interpolation maps were used to visualize the trend of the epidemic spread of JE, and linear regression models were used to calculate the monthly changes in minimum longitude and maximum latitude of emerging towns with JE to assess the speed of the epidemic's spread to the northwest. Additionally, we utilized a time series Seasonal Autoregressive Integrated Moving Average (SARIMA) model to dynamically predict the ongoing weekly number of JE emerging townships. Results: The Kriging difference map revealed a significant trend of JE spread towards the northwest. Our regression model indicated that the rate of decrease in minimum longitude was approximately 0.64 km per month, while the rate of increase in maximum latitude was approximately 1.00 km per month. Furthermore, the SARIMA pattern (2,0,0)(2,0,1)52 exhibited a better goodness-of-fit for predicting JE transmission, with an overall agreement of 93.27% to 94.23%. Conclusion: Our study highlights the expansion of JE cases towards the northwest of Gansu, indicating the need for ongoing surveillance and control efforts. The use of the SARIMA model provides a valuable tool for predicting the trend of JE spatial dispersion, thereby improving early warning systems. Our findings suggest that the number of emerging townships can be used to predict the trend of JE spatial dispersion, providing crucial insights for future research on JE incidence.

Epidemic Features of COVID-19 and Potential Impact of Hospital Strain During the Omicron Wave - Australia, 2022.

What is already known about this topic?: Hospitals have experienced a surge in admissions due to the increasing number of Omicron cases. Understanding the epidemiological features of coronavirus disease 2019 (COVID-19) and the strain it places on hospitals will provide scientific evidence to help policymakers better prepare for and respond to future outbreaks. What is added by this report?: The case fatality rate of COVID-19 was 1.4 per 1,000 persons during the Omicron wave. Over 90% of COVID-19-related deaths occurred in individuals aged 60 years or older, with pre-existing chronic conditions such as cardiac conditions and dementia, particularly among males aged 80 years or older. What are the implications for public health practice?: Public health policy is essential for preparing and preserving medical resource capacity, as well as recruiting additional clinicians and front-line staff in hospitals to address the increased demand. High-risk individuals should be prioritized for healthcare, vaccines, and targeted interventions.

Built environment and schizophrenia re-hospitalization risk in China: A cohort study.

BACKGROUND: Built environment exposure, characterized by ubiquity and changeability, has the potential to be the prospective target of public health policy. However, little research has been conducted to explore its impact on schizophrenia. This study aimed to investigate the association between built environmentand and schizophrenia rehospitalization by simultaneously considering substantial built environmental exposures. METHODS: We recruited eligible schizophrenia patients from Hefei, Anhui Province, China between 2017 and 2019. The main outcome for this study was the time interval until the first recurrent hospital admission occurred within one year after discharge. For each included subject, we estimated the built environment exposures, including population density, walkability, land use mix, green and blue space, public transportation accessibility and road traffic indicator. Lasso (Least Absolute Shrinkage and Selection Operator) analysis was used to select the key variables. Multivariable Cox regression model was applied to obtain hazard ratio (HR) and its corresponding 95% confidence intervals (CI). Further, we also evaluated the joint effects of built environment characteristics on rehospitalization for schizophrenia by Quantile g-computation model. RESULTS: A total of 1564 hospitalized schizophrenia patients were enrolled, with 347 patients (22.2%) had a rehospitalization within one-year after discharge. Multivariable Cox regression analysis indicated that the re-hospitalization rate for schizophrenia would be higher in areas with a high population density (HR: 1.10, 95%CI: 1.04-1.16). Nonetheless, compared to the reference (Q1), participants who lived in a neighborhood with the highest walkability and NDVI (Normalized Difference Vegetation Index) (Q4) had a 76% and 47% lower risk of re-hospitalization within one year (HR:0.24, 95%CI: 0.13-0.45; and 0.53, 95%CI:0.32-0.85), respectively. Moreover, quantile-based g-computation analyses revealed that increased walkability and green space significantly eliminated the adverse effects of population density increases on schizophrenia patients, with a HR ratio of 0.61 (95%CI:0.48,0.79) per one quartile change at the same time. CONCLUSION: Our study provides scientific evidence for the significant role of built environment in schizophrenia rehospitalization, suggesting that optimizing the built environment is required in designing and building a healthy city.

Demographic characteristics, clinical symptoms, biochemical markers and probability of occurrence of severe dengue: A multicenter hospital-based study in Bangladesh.

Establishing reliable early warning models for severe dengue cases is a high priority to facilitate triage in dengue-endemic areas and optimal use of limited resources. However, few studies have identified the complex interactive relationship between potential risk factors and severe dengue. This research aimed to assess the potential risk factors and detect their high-order combinative effects on severe dengue. A structured questionnaire was used to collect detailed dengue outbreak data from eight representative hospitals in Dhaka, Bangladesh, in 2019. Logistic regression and machine learning models were used to examine the complex effects of demographic characteristics, clinical symptoms, and biochemical markers on severe dengue. A total of 1,090 dengue cases (158 severe and 932 non-severe) were included in this study. Dyspnoea (Odds Ratio [OR] = 2.87, 95% Confidence Interval [CI]: 1.72 to 4.77), plasma leakage (OR = 3.61, 95% CI: 2.12 to 6.15), and hemorrhage (OR = 2.33, 95% CI: 1.46 to 3.73) were positively and significantly associated with the occurrence of severe dengue. Classification and regression tree models showed that the probability of occurrence of severe dengue cases ranged from 7% (age >12.5 years without plasma leakage) to 92.9% (age ≤12.5 years with dyspnoea and plasma leakage). The random forest model indicated that age was the most important factor in predicting severe dengue, followed by education, plasma leakage, platelet, and dyspnoea. The research provides new evidence to identify key risk factors contributing to severe dengue cases, which could be beneficial to clinical doctors to identify and predict the severity of dengue early.

Using weather factors and google data to predict COVID-19 transmission in Melbourne, Australia: A time-series predictive model.

Background: Forecast models have been essential in understanding COVID-19 transmission and guiding public health responses throughout the pandemic. This study aims to assess the effect of weather variability and Google data on COVID-19 transmission and develop multivariable time series AutoRegressive Integrated Moving Average (ARIMA) models for improving traditional predictive modelling for informing public health policy. Methods: COVID-19 case notifications, meteorological factors and Google data were collected over the B.1.617.2 (Delta) outbreak in Melbourne, Australia from August to November 2021. Timeseries cross-correlation (TSCC) was used to evaluate the temporal correlation between weather factors, Google search trends, Google Mobility data and COVID-19 transmission. Multivariable time series ARIMA models were fitted to forecast COVID-19 incidence and Effective Reproductive Number (R eff ) in the Greater Melbourne region. Five models were fitted to compare and validate predictive models using moving three-day ahead forecasts to test the predictive accuracy for both COVID-19 incidence and R eff over the Melbourne Delta outbreak. Results: Case-only ARIMA model resulted in an R squared (R2) value of 0.942, Root Mean Square Error (RMSE) of 141.59, and Mean Absolute Percentage Error (MAPE) of 23.19. The model including transit station mobility (TSM) and maximum temperature (Tmax) had greater predictive accuracy with R2 0.948, RMSE 137.57, and MAPE 21.26. Conclusion: Multivariable ARIMA modelling for COVID-19 cases and R eff was useful for predicting epidemic growth, with higher predictive accuracy for models including TSM and Tmax. These results suggest that TSM and Tmax would be useful for further exploration for developing weather-informed early warning models for future COVID-19 outbreaks with potential application for the inclusion of weather and Google data with disease surveillance in developing effective early warning systems for informing public health policy and epidemic response.

Air pollution and liver cancer: A systematic review.

Air pollution has previously been linked to several adverse health outcomes, but the potential association between air pollution and liver cancer remains unclear. We searched PubMed, EMBASE, and Web of Science from inception to 10 October 2021, and manually reviewed the references of relevant papers to further identify any related literature investigating possible associations between air pollution and liver cancer. Risk estimates values were represented by statistical associations based on quantitative analyses. A total of 13 cohort studies obtained from 11 articles were included, with 10,961,717 participants. PM2.5 was the most frequently examined pollutant (included in 11 studies), followed by NO2 and NOx (included in 6 studies), and fewer studies focused on other pollutants (PM2.5 absorbance, PM10, PM2.5-10, O3, and BC). In all the 16 associations for liver cancer mortality, 14 associations reported the effect of PM2.5 on liver cancer mortality. Eight associations on PM2.5 were significant, showing a suggestive association between PM2.5 and liver cancer mortality. Among 24 associations shown by risk estimates for liver cancer incidence, most associations were not statistically significant. For other air pollutants, no positive associations were presented in these studies. PM2.5 was the most frequently examined pollutant, followed by NO2 and NOx, and fewer studies focused on other pollutants. PM2.5 was associated with liver cancer mortality, but there was no association for other air pollutants. Future research should use advanced statistical methods to further assess the impact of multiple air pollutants on liver cancer in the changing socio-environmental context.

Does global warming increase the risk of liver cancer in Australia? Perspectives based on spatial variability.

Australia has experienced an astonishing increase in liver cancer over the past few decades and the epidemiological reasons behind this are puzzling. The existing recognized risk factors for liver cancer, viral hepatitis, and alcohol consumption, are inconsistent with the trend in liver cancer. Behind the effects of migration and metabolic disease lies a potential contribution of climate change to an increase in liver cancer. This study explored the climate-associated distribution of high-risk areas for liver cancer by comparing liver cancer to lung cancer and finds that the incidence of liver cancer is more pronounced in hot and humid areas. This study showed the risk of liver cancer was higher in the equatorial region and tropical regions. These results will extend the study on the health consequences of climate change and provide more ideas and directions for future researchers.

Japanese Encephalitis Emergence in Australia: The Potential Population at Risk.

In Australia, Japanese encephalitis virus circulated in tropical north Queensland between 1995 and 2005. In 2022, a dramatic range expansion across the southern states has resulted in 30 confirmed human cases and 6 deaths. We discuss the outbreak drivers and estimate the potential size of the human population at risk.