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.

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.

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.

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.

Prototypes virus of hand, foot and mouth disease infections and severe cases in Gansu, China: a spatial and temporal analysis.

BACKGROUND: Little research has been conducted on the spatio-temporal relationship between the severe cases and the enteroviruses infections of hand, foot and mouth disease (HFMD). This study aimed to investigate epidemic features and spatial clusters of HFMD incidence rates and assess the relationship between Enterovirus 71 (EV71) and Coxsackievirus A16 (CoxA16) and severe cases of HMFD in Gansu province, China. METHODS: Weekly county-specific data on HFMD between 1st January and 31st December 2018 were collected from the China Infectious Disease Information System (CIDIS), including enterovirus type (EV71 and CoxA16), severe and non-severe cases in Gansu province, China. Temporal risk [frequency index (α), duration index (ß) and intensity index (γ)] and spatial cluster analysis were used to assess epidemic features and identify high-risk areas for HFMD. Time-series cross-correlation function and regression model were used to explore the relationship between the ratios of two types of viruses (i.e. EV71/Cox16) (EC) and severe cases index (i.e. severe cases/non-severe cases) (SI) of HFMD. RESULTS: Some counties in Dingxi City, Gansu were identified as a hot spot for the temporal risk indices. Time-series cross-correlation analysis showed that SI was significantly associated with EC (r = 0.417, P < 0.05) over a 4-week time lag. The regression analysis showed that SI was positively associated with EC (ß = 0.04, 95% confidence interval (CI) 0.02-0.06). CONCLUSION: The spatial patterns of HFMD incidence were associated with enteroviruses in Gansu. The research suggested that the EC could be considered a potential early warning sign for predicting severe cases of HFMD in Gansu province.

Future perspectives of emerging infectious diseases control: A One Health approach.

Since the beginning of the COVID-19 pandemic in early 2020, global efforts to respond to and control COVID-19 have varied widely with some countries, including Australia, successfully containing local transmission, and minimising negative impacts to health and economies. Over this time, global awareness of climate variability due to climate change and the risk factors for emerging infectious diseases transmission has increased alongside an understanding of the inextricable relationship between the health of the environment, humans, and animals. Overall, the global response to the current pandemic suggests there is an urgent need for a One Health approach in controlling and preventing future pandemics, through developing integrated, dynamic, spatiotemporal early warning systems based on a One Health approach for emerging infectious diseases.

Weather variability and transmissibility of COVID-19: a time series analysis based on effective reproductive number.

COVID-19 is causing a significant burden on medical and healthcare resources globally due to high numbers of hospitalisations and deaths recorded as the pandemic continues. This research aims to assess the effects of climate factors (i.e., daily average temperature and average relative humidity) on effective reproductive number of COVID-19 outbreak in Wuhan, China during the early stage of the outbreak. Our research showed that effective reproductive number of COVID-19 will increase by 7.6% (95% Confidence Interval: 5.4% ~ 9.8%) per 1°C drop in mean temperature at prior moving average of 0-8 days lag in Wuhan, China. Our results indicate temperature was negatively associated with COVID-19 transmissibility during early stages of the outbreak in Wuhan, suggesting temperature is likely to effect COVID-19 transmission. These results suggest increased precautions should be taken in the colder seasons to reduce COVID-19 transmission in the future, based on past success in controlling the pandemic in Wuhan, China.

Weather Variability and COVID-19 Transmission: A Review of Recent Research.

Weather and climate play a significant role in infectious disease transmission, through changes to transmission dynamics, host susceptibility and virus survival in the environment. Exploring the association of weather variables and COVID-19 transmission is vital in understanding the potential for seasonality and future outbreaks and developing early warning systems. Previous research examined the effects of weather on COVID-19, but the findings appeared inconsistent. This review aims to summarize the currently available literature on the association between weather and COVID-19 incidence and provide possible suggestions for developing weather-based early warning system for COVID-19 transmission. Studies eligible for inclusion used ecological methods to evaluate associations between weather (i.e., temperature, humidity, wind speed and rainfall) and COVID-19 transmission. The review showed that temperature was reported as significant in the greatest number of studies, with COVID-19 incidence increasing as temperature decreased and the highest incidence reported in the temperature range of 0-17 °C. Humidity was also significantly associated with COVID-19 incidence, though the reported results were mixed, with studies reporting positive and negative correlation. A significant interaction between humidity and temperature was also reported. Wind speed and rainfall results were not consistent across studies. Weather variables including temperature and humidity can contribute to increased transmission of COVID-19, particularly in winter conditions through increased host susceptibility and viability of the virus. While there is less indication of an association with wind speed and rainfall, these may contribute to behavioral changes that decrease exposure and risk of infection. Understanding the implications of associations with weather variables and seasonal variations for monitoring and control of future outbreaks is essential for early warning systems.