Lag effect of temperature on the incidence of COVID-19 in Hunan province

Objective To explore the lag effect of daily average temperature on the incidence of coronavirus disease 2019 (COVID-19) in Hunan Province and to provide scientific evidences for effective prevention of COVID-19.  Methods  The meteorological factors, the air quality factors and the data conincidence of COVID-19 reported in Hunan Province during January 21, 2020 to March 2, 2020 were collected. Spearman correlation and distributed lag non-linear model analysis were performed.  Results  A total of 1 018 COVID-19 cases were reported in Hunan Province. The distribution lag non-linear model results showed that the influence of daily average temperature on the incidence of COVID-19 presented a nonlinear relationship. The cumulative relative incidence risk of COVID-19 decreased with the increase of daily average temperature, and the lowest temperature risk of the patients was 0 ℃. Both cold temperature and hot temperature increased incidence risk of COVID-19. It was indicated that the hot effects were immediate, however, the cold effects with obvious lag effect persisted up to 12 days. The highest relative risk of COVID-19 incidence was associated with lag 8-day daily average temperature of -5 ℃(RR=2.20, 95% CI=1.16-4.19). The influence of high temperature(10 ℃) was more significant than that of low temperature(6 ℃).  Conclusion  The daily average temperature, especially cold or hot temperature, was an important influencing factor of the incidence of COVID-19 in Hunan Province, which had lag influence on the incidence of COVID-19. We suggested that some related preventive measures should be adopted to protect vulnerable population and severe patients to reduce the incidence risk. © 2021, Publication Centre of Anhui Medical University. All rights reserved.

Real-time Prediction of the Daily Incidence of COVID-19 in 215 Countries and Territories Using Machine Learning: Model Development and Validation.

BACKGROUND: Advanced prediction of the daily incidence of COVID-19 can aid policy making on the prevention of disease spread, which can profoundly affect people's livelihood. In previous studies, predictions were investigated for single or several countries and territories. OBJECTIVE: We aimed to develop models that can be applied for real-time prediction of COVID-19 activity in all individual countries and territories worldwide. METHODS: Data of the previous daily incidence and infoveillance data (search volume data via Google Trends) from 215 individual countries and territories were collected. A random forest regression algorithm was used to train models to predict the daily new confirmed cases 7 days ahead. Several methods were used to optimize the models, including clustering the countries and territories, selecting features according to the importance scores, performing multiple-step forecasting, and upgrading the models at regular intervals. The performance of the models was assessed using the mean absolute error (MAE), root mean square error (RMSE), Pearson correlation coefficient, and Spearman correlation coefficient. RESULTS: Our models can accurately predict the daily new confirmed cases of COVID-19 in most countries and territories. Of the 215 countries and territories under study, 198 (92.1%) had MAEs <10 and 187 (87.0%) had Pearson correlation coefficients >0.8. For the 215 countries and territories, the mean MAE was 5.42 (range 0.26-15.32), the mean RMSE was 9.27 (range 1.81-24.40), the mean Pearson correlation coefficient was 0.89 (range 0.08-0.99), and the mean Spearman correlation coefficient was 0.84 (range 0.2-1.00). CONCLUSIONS: By integrating previous incidence and Google Trends data, our machine learning algorithm was able to predict the incidence of COVID-19 in most individual countries and territories accurately 7 days ahead.

Spatially Refined Time-Varying Reproduction Numbers of COVID-19 by Health District in Georgia, USA, March-December 2020.

This study quantifies the transmission potential of SARS-CoV-2 across public health districts in Georgia, USA, and tests if per capita cumulative case count varies across counties. To estimate the time-varying reproduction number, Rt of SARS-CoV-2 in Georgia and its 18 public health districts, we apply the R package 'EpiEstim' to the time series of historical daily incidence of confirmed cases, 2 March-15 December 2020. The epidemic curve is shifted backward by nine days to account for the incubation period and delay to testing. Linear regression is performed between log10-transformed per capita cumulative case count and log10-transformed population size. We observe Rt fluctuations as state and countywide policies are implemented. Policy changes are associated with increases or decreases at different time points. Rt increases, following the reopening of schools for in-person instruction in August. Evidence suggests that counties with lower population size had a higher per capita cumulative case count on June 15 (slope = -0.10, p = 0.04) and October 15 (slope = -0.05, p = 0.03), but not on August 15 (slope = -0.04, p = 0.09), nor December 15 (slope = -0.02, p = 0.41). We found extensive community transmission of SARS-CoV-2 across all 18 health districts in Georgia with median 7-day-sliding window Rt estimates between 1 and 1.4 after March 2020.

COVID-19 incidence and mortality in Nigeria: gender based analysis.

BACKGROUND: Coronavirus Disease 2019 (COVID-19) has been surging globally. Risk strata in medical attention are of dynamic significance for apposite assessment and supply distribution. Presently, no known cultured contrivance is available to fill this gap of this pandemic. The aim of this study is to develop a predictive model based on vector autoregressive moving average (VARMA) model of various orders for gender based daily COVID-19 incidence in Nigeria. This study also aims to proffer empirical evidence that compares incidence between male and female for COVID-19 risk factors. METHODS: Wilcoxon signed-rank test is employed to investigate the significance of the difference in the gender distributions of the daily incidence. A VARMA model of various orders is formulated for the gender based daily COVID-19 incidence in Nigeria. The optimal VARMA model is identified using Bayesian information criterion. Also, a predictive model based on univariate autoregressive moving average model is formulated for the daily death cases in Nigeria. Fold change is estimated based on crude case-fatality risk to investigate whether there is massive underreporting and under-testing of COVID-19 cases in Nigeria. RESULTS: Daily incidence is higher in males on most days from 11 April 2020 to 12 September 2020. Result of Wilcoxon signed-rank test shows that incidence among male is significantly higher than female (p-value < 2.22 × 10-16). White neural network test shows that daily female incidence is not linear in mean (p-value = 0.00058746) while daily male incidence is linear in mean (p-value = 0.4257). McLeod-Li test shows that there is autoregressive conditional heteroscedasticity in the female incidence (Maximum p-value = 1.4277 × 10-5) and male incidence (Maximum p-value = 9.0816 × 10-14) at 5% level of significance. Ljung-Box test (Tsay, 2014) shows that the daily incidence cases are not random (p-value=0.0000). The optimal VARMA model for male and female daily incidence is VARMA (0,1). The optimal model for the Nigeria's daily COVID-19 death cases is identified to be ARIMA (0,1,1). There is no evidence of massive underreporting and under-testing of COVID-19 cases in Nigeria. CONCLUSIONS: Comparison of the observed incidence with fitted data by gender shows that the optimal VARMA and ARIMA models fit the data well. Findings highlight the significant roles of gender on daily COVID-19 incidence in Nigeria.