Dataset Information

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

ABSTRACT:

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.

SUBMITTER: McClymont H

PROVIDER: S-EPMC9941072 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

McClymont Hannah H Si Xiaohan X Hu Wenbiao W

Heliyon 20230221 3

<h4>Background</h4>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.<h4>Methods</h4>COVID-19 case notifications, meteorological factors ...[more]

PMID: 36845036

Similar Datasets

Project description:BackgroundDengue is a re-emerging infectious disease of humans, rapidly growing from endemic areas to dengue-free regions due to favorable conditions. In recent decades, Guangzhou has again suffered from several big outbreaks of dengue; as have its neighboring cities. This study aims to examine the impact of dengue epidemics in Guangzhou, China, and to develop a predictive model for Zhongshan based on local weather conditions and Guangzhou dengue surveillance information.MethodsWe obtained weekly dengue case data from 1st January, 2005 to 31st December, 2014 for Guangzhou and Zhongshan city from the Chinese National Disease Surveillance Reporting System. Meteorological data was collected from the Zhongshan Weather Bureau and demographic data was collected from the Zhongshan Statistical Bureau. A negative binomial regression model with a log link function was used to analyze the relationship between weekly dengue cases in Guangzhou and Zhongshan, controlling for meteorological factors. Cross-correlation functions were applied to identify the time lags of the effect of each weather factor on weekly dengue cases. Models were validated using receiver operating characteristic (ROC) curves and k-fold cross-validation.ResultsOur results showed that weekly dengue cases in Zhongshan were significantly associated with dengue cases in Guangzhou after the treatment of a 5 weeks prior moving average (Relative Risk (RR) = 2.016, 95% Confidence Interval (CI): 1.845-2.203), controlling for weather factors including minimum temperature, relative humidity, and rainfall. ROC curve analysis indicated our forecasting model performed well at different prediction thresholds, with 0.969 area under the receiver operating characteristic curve (AUC) for a threshold of 3 cases per week, 0.957 AUC for a threshold of 2 cases per week, and 0.938 AUC for a threshold of 1 case per week. Models established during k-fold cross-validation also had considerable AUC (average 0.938-0.967). The sensitivity and specificity obtained from k-fold cross-validation was 78.83% and 92.48% respectively, with a forecasting threshold of 3 cases per week; 91.17% and 91.39%, with a threshold of 2 cases; and 85.16% and 87.25% with a threshold of 1 case. The out-of-sample prediction for the epidemics in 2014 also showed satisfactory performance.ConclusionOur study findings suggest that the occurrence of dengue outbreaks in Guangzhou could impact dengue outbreaks in Zhongshan under suitable weather conditions. Future studies should focus on developing integrated early warning systems for dengue transmission including local weather and human movement.

Project description:OBJECTIVE:To establish the prevalence of and risk factors for Chlamydia trachomatis infection to determine the role of universal versus targeted testing. METHODS:A prospective study of 1107 women attending two sexual and reproductive health clinics in Melbourne, Australia, was carried out. A questionnaire was used to establish risk factors. Urine samples were tested for C. trachomatis by PCR. The main outcome measures were prevalence of and risk factors for C. trachomatis infection. RESULTS:Of 1107 recruitable women, 851 (76.9%) consented and were successfully tested. C. trachomatis was detected in 18 (4.8% (95% CI 2.9 to 7.5)) of 373 women in the inner city and eight (1.7% (95% CI (0.7 to 3.3)) of 478 women in the suburban clinic. Of women under 25 years, 17 (6.2% (95% CI 3.7 to 9.8)) of 273 in the inner city in contrast with three (1.7% (95% CI 0.4 to 5.0)) of 174 in the suburban clinic were infected. In the inner city clinic, age under 25 years (OR 5.4 (95% CI 0.7 to 41.5)), vaginal discharge (OR 4.1 (95% CI 1.5 to 11.1)), and recent change of sexual partner (OR 4.6 (95% CI 1.6 to 12.9)) were associated with C. trachomatis. In contrast, in the suburban clinic, only vaginal discharge (OR 3.5 (95% CI 0.9 to 14.3)) and recent change of sexual partner (OR 3.4 (95% CI 0.8 to 15.7)) were identified as risk factors. Multivariate analysis showed that recent change of partner (OR 4.5 (95% CI 1.5 to 13.8)) was the most strongly associated independent risk factor for infection in the inner city clinic. CONCLUSION:The high prevalence of C. trachomatis indicates that universal testing should be undertaken in the inner city clinic. Young age may not be a risk factor for C. trachomatis in more affluent populations with lower prevalence rates. No risk factors were identified with sufficient sensitivity and specificity to be useful for targeted testing. Prevalence and identifiable risk factors for C. trachomatis are not transferable between populations, even in the same city.

Project description:BackgroundThe past decade witnessed an increment in the incidence of hand foot mouth disease (HFMD) in the Pacific Asian region; specifically, in Guangzhou China. This emphasized the requirement of an early warning system designed to allow the medical community to better prepare for outbreaks and thus minimize the number of fatalities.MethodsSamples from 1,556 inpatients (hospitalized) and 11,004 outpatients (non-admitted) diagnosed with HFMD were collected in this study from January 2009 to October 2013. Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied to establish high predictive model for inpatients and outpatient as well as three viral serotypes (EV71, Pan-EV and CA16). To integrate climate variables in the data analyses, data from eight climate variables were simultaneously obtained during this period. Significant climate variable identified by correlation analyses was executed to improve time series modeling as external repressors.ResultsAmong inpatients with HFMD, 248 (15.9%) were affected by EV71, 137 (8.8%) were affected by Pan-EV+, and 436 (28.0%) were affected by CA16. Optimal Univariate SARIMA model was identified: (2,0,3)(1,0,0)52 for inpatients, (0,1,0)(0,0,2)52 for outpatients as well as three serotypes (EV71, (1,0,1)(0,0,1)52; CA16, (1,0,1)(0,0,0)52; Pan-EV, (1,0,1)(0,0,0)52). Using climate as our independent variable, precipitation (PP) was first identified to be associated with inpatients (r = 0.211, P = 0.001), CA16-serotype (r = 0.171, P = 0.007) and outpatients (r = 0.214, P = 0.01) in partial correlation analyses, and was then shown a significant lag in cross-autocorrelation analyses. However, inclusion of PP [lag -3 week] as external repressor showed a moderate impact on the predictive performance of the SARIMA model described here-in.ConclusionClimate patterns and HFMD incidences have been shown to be strongly correlated. The SARIMA model developed here can be a helpful tool in developing an early warning system for HFMD.

Dataset Information

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

Background

Methods

Results

Conclusion

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets