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.

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.

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.

Age-period-cohort analysis of lung cancer mortality in China and Australia from 1990 to 2019.

Lung cancer (LC) is the leading cause of cancer death in China and Australia, the countries with different socioenvironmental contexts in the Western Pacific Region. Comparing the age-period-cohort effect on LC mortality (LCM) between the two countries can help plan interventions and draw lessons for countries in the region. We collected LCM estimates between 1990 and 2019 from the GBD 2019. Age-period-cohort modelling was applied to compute the net drift, local drift, cross-sectional age curve, longitudinal age curve, and the rate ratios (RRs) of period and cohort. China had a higher LC age-standardized mortality rate than Australia in 2019 (men: 58.10 [95% uncertainty interval (UI): 46.53, 70.89] vs. 30.13 [95% UI: 27.88, 32.31]/100,000 population; women: 22.86 [95% UI: 18.52, 27.52] vs. 17.80 [95% UI: 15.93, 19.34]/100,000 population). Period and cohort effects on LCM improved more markedly among Australian men (RR for period effect, from 1.47 [95% confidence interval (CI) 1.41, 1.53] to 0.79 [95% CI 0.75, 0.84]; RR for cohort effect, from 2.56 [95% CI 2.44, 2.68] to 0.36 [95% CI 0.11, 1.18]) and Chinese women (RR for period effect, from 1.06 [95% CI 1.01, 1.11] to 0.85 [95% CI 0.82, 0.89]; RR for cohort effect, from 0.71 [95% CI 0.65, 0.78] to 0.51 [95% CI 0.26, 1.03]) during the study period and birth cohort. The LCM in Chinese population aged 65 to 79 and Australian women aged 75 to 79 increased. Smoking and particulate matter (PM) contributed most to LCM in China, while smoking and occupational carcinogens contributed most in Australia. Decreasing period and cohort risks for LCM attributable to smoking and PM were more remarkable in Australia than in China. The LCM attributable to occupational carcinogens was higher in Australia than in China, particularly for those aged 60 to 79. Vigorous tobacco and PM control, which brought a substantial decline in LCM in Australia, may help reduce LCM in China. Australia should highlight LC prevention among people with occupational exposure. Chinese aged ≥ 65 and Australian women aged ≥ 75 should be the priorities for LC interventions.

Co-benefits of nonpharmaceutical intervention against COVID-19 on infectious diseases in China: A large population-based observational study.

Background: Nonpharmaceutical interventions (NPIs) are public health measures that aim to suppress the transmission of infectious diseases, including border restrictions, quarantine and isolation, community management, social distancing, face mask usage, and personal hygiene. This research aimed to assess the co-benefits of NPIs against COVID-19 on notifiable infectious diseases (NIDs) in Guangdong Province, China. Methods: Based on NID data from the Notifiable Infectious Diseases Surveillance System in Guangdong, we first compared the incidence of NIDs during the emergency response period (weeks 4-53 of 2020) with those in the same period of 2015-2019 and then compared that with the expected incidence during the synchronous period of 2020 for each city by using a Bayesian structural time series model. Findings: A total of 514,341 cases of 39 types of NIDs were reported in Guangdong during the emergency response period in 2020, which decreased by 50·7% compared with the synchronous period during 2015-2019. It was estimated that the number of 39 NIDs during the emergency response in 2020 was 65·6% (95% credible interval [CI]: 64·0% - 68·2%) lower than expected, which means that 982,356 (95% CI: 913,443 - 1,105,170) cases were averted. The largest reduction (82·1%) was found for children aged 0-14 years. For different categories of NIDs, natural focal diseases and insect-borne infectious diseases had the greatest reduction (89·4%), followed by respiratory infectious diseases (87·4%), intestinal infectious diseases (59·4%), and blood-borne and sexually transmitted infections (18·2%). Dengue, influenza, and hand-foot-and-mouth disease were reduced by 99·3%, 95·1%, and 76·2%, respectively. Larger reductions were found in the regions with developed economies and a higher number of COVID-19 cases. Interpretation: NPIs against COVID-19 may have a large co-benefit on the prevention of other infectious diseases in Guangdong, China, and the effects have heterogeneity in populations, diseases, time and space. Funding: Key-Area Research and Development Program of Guangdong Province.

Rapid shortening of survival duration in early fatal cases of COVID-19, Wuhan, China.

Severe COVID-19 cases place immediate pressure on hospital resources. To assess this, we analysed survival duration in the first 39 fatal cases in Wuhan, China. Time from onset and hospitalization to death declined rapidly, from ~40 to 7 days, and ~25 to 4 days, respectively, in the outbreak's first month.

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.

Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic.

BACKGROUND: The global impact of COVID-19 and the country-specific responses to the pandemic provide an unparalleled opportunity to learn about different patterns of the outbreak and interventions. We model the global pattern of reported COVID-19 cases during the primary response period, with the aim of learning from the past to prepare for the future. METHODS: Using Bayesian methods, we analyse the response to the COVID-19 outbreak for 158 countries for the period 22 January to 9 June 2020. This encompasses the period in which many countries imposed a variety of response measures and initial relaxation strategies. Instead of modelling specific intervention types and timings for each country explicitly, we adopt a stochastic epidemiological model including a feedback mechanism on virus transmission to capture complex nonlinear dynamics arising from continuous changes in community behaviour in response to rising case numbers. We analyse the overall effect of interventions and community responses across diverse regions. This approach mitigates explicit consideration of issues such as period of infectivity and public adherence to government restrictions. RESULTS: Countries with the largest cumulative case tallies are characterised by a delayed response, whereas countries that avoid substantial community transmission during the period of study responded quickly. Countries that recovered rapidly also have a higher case identification rate and small numbers of undocumented community transmission at the early stages of the outbreak. We also demonstrate that uncertainty in numbers of undocumented infections dramatically impacts the risk of multiple waves. Our approach is also effective at pre-empting potential flare-ups. CONCLUSIONS: We demonstrate the utility of modelling to interpret community behaviour in the early epidemic stages. Two lessons learnt that are important for the future are: i) countries that imposed strict containment measures early in the epidemic fared better with respect to numbers of reported cases; and ii) broader testing is required early in the epidemic to understand the magnitude of undocumented infections and recover rapidly. We conclude that clear patterns of containment are essential prior to relaxation of restrictions and show that modelling can provide insights to this end.

Live poultry market closure and avian influenza A (H7N9) infection in cities of China, 2013-2017: an ecological study.

BACKGROUND: Previous studies have proven that the closure of live poultry markets (LPMs) was an effective intervention to reduce human risk of avian influenza A (H7N9) infection, but evidence is limited on the impact of scale and duration of LPMs closure on the transmission of H7N9. METHOD: Five cities (i.e., Shanghai, Suzhou, Shenzhen, Guangzhou and Hangzhou) with the largest number of H7N9 cases in mainland China from 2013 to 2017 were selected in this study. Data on laboratory-confirmed H7N9 human cases in those five cities were obtained from the Chinese National Influenza Centre. The detailed information of LPMs closure (i.e., area and duration) was obtained from the Ministry of Agriculture. We used a generalized linear model with a Poisson link to estimate the effect of LPMs closure, reported as relative risk reduction (RRR). We used classification and regression trees (CARTs) model to select and quantify the dominant factor of H7N9 infection. RESULTS: All five cities implemented the LPMs closure, and the risk of H7N9 infection decreased significantly after LPMs closure with RRR ranging from 0.80 to 0.93. Respectively, a long-term LPMs closure for 10-13 weeks elicited a sustained and highly significant risk reduction of H7N9 infection (RRR = 0.98). Short-time LPMs closure with 2 weeks in every epidemic did not reduce the risk of H7N9 infection (p > 0.05). Partially closed LPMs in some suburbs contributed only 35% for reduction rate (RRR = 0.35). Shenzhen implemented partial closure for first 3 epidemics (p > 0.05) and all closure in the latest 2 epidemic waves (RRR = 0.64). CONCLUSION: Our findings suggest that LPMs all closure in whole city can be a highly effective measure comparing with partial closure (i.e. only urban closure, suburb and rural remain open). Extend the duration of closure and consider permanently closing the LPMs will help improve the control effect. The effect of LPMs closure seems greater than that of meteorology on H7N9 transmission.