Daily New Cases Of COVID-19 Research Articles

The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis.

BackgroundThe influence of meteorological factors on the transmission and spread of COVID-19 is of interest and has not been investigated.ObjectiveThis study aimed to investigate the associations between meteorological factors and the daily number of new cases of COVID-19 in 9 Asian cities.MethodsPearson correlation and generalized additive modeling (GAM) were performed to assess the relationships between daily new COVID-19 cases and meteorological factors (daily average temperature and relative humidity) with the most updated data currently available.ResultsThe Pearson correlation showed that daily new confirmed cases of COVID-19 were more correlated with the average temperature than with relative humidity. Daily new confirmed cases were negatively correlated with the average temperature in Beijing (r=–0.565, P<.001), Shanghai (r=–0.47, P<.001), and Guangzhou (r=–0.53, P<.001). In Japan, however, a positive correlation was observed (r=0.416, P<.001). In most of the cities (Shanghai, Guangzhou, Hong Kong, Seoul, Tokyo, and Kuala Lumpur), GAM analysis showed the number of daily new confirmed cases to be positively associated with both average temperature and relative humidity, especially using lagged 3D modeling where the positive influence of temperature on daily new confirmed cases was discerned in 5 cities (exceptions: Beijing, Wuhan, Korea, and Malaysia). Moreover, the sensitivity analysis showed, by incorporating the city grade and public health measures into the model, that higher temperatures can increase daily new case numbers (beta=0.073, Z=11.594, P<.001) in the lagged 3-day model.ConclusionsThe findings suggest that increased temperature yield increases in daily new cases of COVID-19. Hence, large-scale public health measures and expanded regional research are still required until a vaccine becomes widely available and herd immunity is established.

Comparison of phenomenological growth models in predicting cumulative number of COVID-19 cases in East Java Province, Indonesia

In this paper, five phenomenological (Richards, a generalized Richards, Blumberg, Tsoularis & Wallace, and Gompertz) models are implemented to predict the cumulative number of COVID-19 cases. The five phenomenological models are in the form of ordinary differential equations with a few number of model parameters. The model parameters of each model were calibrated by fitting the model with the reported cumulative number of COVID-19 cases in East Java Province from March 25 until October 31, 2020 via nonlinear least square method. We compare the performance of the five phenomenological models by measuring four performance metrics, namely the root mean square error (RMSE), the mean absolute error (MAE), the coefficient of determination (R2) and the Akaike information criterion (AIC). When calibrating the cumulative number of cases, the five models perform very well, which are indicated by their high coefficient of determination (R2>0.999). However, a comparison of the four- performance metrics shows that Tsoularis & Wallace performed the best followed by a generalized Richards model. The prediction for the final size of the COVID-19 epidemic in East Java according to the Tsoularis & Wallace model is K=78 002. Both Richards and Gompertz models tend to underestimate the final size of the epidemic, while the Blumberg model tends to overestimate. The five models estimate the peak of the COVID-19 epidemic in East Java has been occurred on August 13-14, 2020. Using the predicted cumulative number of cases, we determine the daily new cases of COVID-19 in East Java. Based on the four-performance metrics, it appears that the five phenomenological models predict new daily cases of COVID-19 equally well.

Coronavirus COVID-19 outbreak and control: Effect of temperature, relative humidity, and lockdown implementation

Meteorological parameters are important factors that have an influence on infectious diseases. The present study aimed to explore the correlation between the spread of COVID-19, temperature, and relative humidity. The effect of human-imposed control parameters in the form of lockdown on the dissipation of COVID-19 was also analysed. Data were collected on the three study variables – temperature, relative humidity, and lockdown period – from nine of the most infected cities worldwide as well as information on changes in the number of COVID-19 patients from the beginning to a specific point in the lockdown period. A generalised regression model was applied to explore the effect of temperature and relative humidity on the change in daily new cases of COVID-19. The regression analysis did not find any significant correlation between temperature, humidity, and change in number of COVID-19 cases. Analysis of the cities with wide-ranging temperature variations showed a negative correlation of COVID-19 transmission (P=0.079) with temperature, but a relatively non-significant correlation with relative humidity (P=0.198). The number of total deaths was also higher in low-temperature countries compared with high-temperature countries. The specific growth rate in COVID-19 cases was decreased by more than 66% after implementation of a lockdown. This growth rate was exponentially decreased over time through the proper implementation of lockdown. Analysis of the real-case scenario and application of predictive models showed that for New York, Lombardy, and Madrid more than 120 days of strict lockdown was required for complete control of the transmission of COVID-19.

Count regression models for COVID-19

At the end of 2019, the current novel coronavirus emerged as a severe acute respiratory disease that has now become a worldwide pandemic. Future generations will look back on this difficult period and see how our society as a whole united and rose to this challenge. Many reports have suggested that this new virus is becoming comparable to the Spanish flu pandemic of 1918. We provide a statistical study on the modelling and analysis of the daily incidence of COVID-19 in eighteen countries around the world. In particular, we investigate whether it is possible to fit count regression models to the number of daily new cases of COVID-19 in various countries and make short term predictions of these numbers. The results suggest that the biggest advantage of these methods is that they are simplistic and straightforward allowing us to obtain preliminary results and an overall picture of the trends in the daily confirmed cases of COVID-19 around the world. The best fitting count regression model for modelling the number of new daily COVID-19 cases of all countries analysed was shown to be a negative binomial distribution with log link function. Whilst the results cannot solely be used to determine and influence policy decisions, they provide an alternative to more specialised epidemiological models and can help to support or contradict results obtained from other analysis.

How Can the Epidemic Curve of COVID-19 in Iran Be Interpreted?

Background : Iran is one of the countries most affected by COVID-19. This review provides possible interpretations of the observed trend of COVID-19 in Iran. Study design : A rapid review Methods: We reviewed the daily new cases of COVID-19 based on hospitalized and outpatients, reported deaths, and diagnostic testing in Iran. Results : Iran reported its first peak in the number of cases in late March, 2020. From the 1 April to 3 May 2020, the downward trend in the number of cases was started. The death trend also showed a peak in early April as well as a downward trend in late April. During May, the number of death cases showed a stable trend with a daily number of deaths ranging between 50 and 75 cases. Then the number of deaths gradually increased. Conclusion : The epidemic curve in Iran is a function of different factors such number of total tests, change in mitigation policies, and heterogeneities among different provinces in the country. Therefore it should be interpreted under the light of the effect of such factors.

How Can the Epidemic Curve of COVID-19 in Iran Be Interpreted?

Background: Iran is one of the countries most affected by COVID-19. This review provides possible interpretations of the observed trend of COVID-19 in Iran. Study design: A rapid review Methods: We reviewed the daily new cases of COVID-19 based on hospitalized and outpatients, reported deaths, and diagnostic testing in Iran. Results: Iran reported its first peak in the number of cases in late March, 2020. From the 1 April to 3 May 2020, the downward trend in the number of cases was started. The death trend also showed a peak in early April as well as a downward trend in late April. During May, the number of death cases showed a stable trend with a daily number of deaths ranging between 50 and 75 cases. Then the number of deaths gradually increased. Conclusion: The epidemic curve in Iran is a function of different factors such number of total tests, change in mitigation policies, and heterogeneities among different provinces in the country. Therefore it should be interpreted under the light of the effect of such factors.

Forecasting of New Cases of COVID-19 in Nigeria Using Autoregressive Fractionally Integrated Moving Average Models

The emergence of global pandemic known as COVID-19 has impacted significantly on human lives and measures have been taken by government all over the world to minimize the rate of spread of the virus, one of which is by enforcing lockdown. In this study, Autoregressive fractionally integrated moving average (ARFIMA) Models was used to model and forecast what the daily new cases of COVID-19 would have been ten days after the lockdown was eased in Nigeria and compare to the actual new cases for the period when the lockdown was eased. The proposed model ARFIMA model was compared with ARIMA (1, 0, 0), and ARIMA (1, 0, 1) and found to outperform the classical ARIMA models based on AIC and BIC values. The results show that the rate of spread of COVID-19 would have been significantly less if the strict lockdown had continued. ARFIMA model was further used to model what new cases of COVID-19 would be ten days ahead starting from 31st of August 2020. Therefore, this study recommends that government should further enforce measures to reduce the spread of the virus if business must continue as usual.

Investigating the Effects of Meteorological Parameters on COVID-19: Case Study of New Jersey, United States

This research aims to explore the correlation between meteorological parameters and COVID-19 pandemic in New Jersey, United States. The authors employ extensive correlation analysis including Pearson correlation, Spearman correlation, Kendall’s rank correlation and auto regressive distributed lag (ARDL) to check the effects of meteorological parameters on the COVID new cases of New Jersey. In doing so, PM 2.5, air quality index, temperature (°C), humidity (%), health security index, human development index, and population density are considered as crucial meteorological and non-meteorological factors. This research work used the maximum available data of all variables from 1st March to 7th July 2020. Among the weather indicators, temperature (°C) was found to have a negative correlation, while humidity and air quality highlighted a positive correlation with daily new cases of COVID-19 in New Jersey. The empirical findings illustrated that there is a strong positive association of lagged humidity, air quality, PM 2.5, and previous infections with daily new cases. Similarly, the ARDL findings suggest that air quality, humidity and infections have lagged effects with the COVID-19 spread across New Jersey. The empirical conclusions of this research might serve as a key input to mitigate the rapid spread of COVID-19 across the United States.

Dynamics identification and forecasting of COVID-19 by switching Kalman filters.

The COVID-19 pandemic has captivated scientific activity since its early days. Particular attention has been dedicated to the identification of underlying dynamics and prediction of future trend. In this work, a switching Kalman filter formalism is applied on dynamics learning and forecasting of the daily new cases of COVID-19. The main feature of this dynamical system is its ability to switch between different linear Gaussian models based on the observations and specified probabilities of transitions between these models. It is thus able to handle the problem of hidden state estimation and forecasting for models with non-Gaussian and nonlinear effects. The potential of this method is explored on the daily new cases of COVID-19 both at the state-level and the country-level in the US. The results suggest a common disease dynamics across states that share certain features. We also demonstrate the ability to make short to medium term predictions with quantifiable error bounds.

Effects of climatological parameters on the outbreak spread of COVID-19 in highly affected regions of Spain.

The coronavirus (COVID-19) pandemic is infecting the human population, killing people, and destroying livelihoods. This research sought to explore the associations of daily average temperature (AT) and air quality (PM2.5) with the daily new cases of COVID-19 in the top four regions of Spain (Castilla y Leon, Castilla-La Mancha, Catalonia, and Madrid). To this end, the authors employ Pearson correlation, Spearman correlation, and robust panel regressions to quantify the overall co-movement between temperature, air quality, and daily cases of COVID-19 from 29 February to 17 July 2020. Overall empirical results show that temperature may not be a determinant to induce COVID-19 spread in Spain, while the rising temperature may reduce the virus transmission. However, the correlation and regression findings illustrate that air quality may speed up the transmission rate of COVID-19. Our findings are contrary to the earlier studies, which show a significant impact of temperature in raising the COVID-19 spread. The conclusions of this work can serve as an input to mitigate the rapid spread of COVID-19 in Spain and reform policies accordingly.

Effect of meteorological parameters on spread of COVID-19 in India and air quality during lockdown

The novel Coronavirus (COVID-19) was identified in Wuhan, Hubei Province, China, in December 2019 and has created a medical emergency worldwide. In India, it is already reported more than 855 thousand cases and more than 22 thousands deaths due to COVID-19 till July 12, 2020. The role of temperature, humidity, and absolute humidity in the transmission of COVID-19 has not yet been well established. In contrast, for the previous many viral infections like influenza, it is well established. Therefore the study to investigate the meteorological condition for incidence and spread of COVID-19 infection and to provide a scientific basis for prevention and control measures against the new disease is required for India. In this work, we analyze daily averaged meteorological data for the last three years (2017–2019) for March, April and May months and the same for the year 2020 for March 1 to May 31. We found a positive association between daily COVID-19 cases and temperature and a mixed association with relative and absolute humidity over India. We have investigated the association of aerosols (AOD) and other pollutions (NO2) with COVID-19 cases during the study period and also during the lockdown period (25 March-31 May) in India. During the lockdown period, aerosols (AOD) and NO2 reduced sharply with a maximum percentage drop of about 60 and 45, respectively. We have also found the reduction in surface PM2.5 PM10 and NO2 for the six mega cities of India during the lockdown period. Our results suggest that COVID-19 still may spread in warm, humid regions or during summer/monsoon, therefore an effective public health intervention should be implemented across India to slow down the transmission of COVID-19.

Scaling Analysis of Daily New Cases of COVID-19 in Some Major Affected Countries

In the present work scaling analysis has been performed over daily number of new cases of COVID-19 for seven mostly affected countries on the basis of total cumulative number of cases till 07 April, 2020 viz. USA, Spain, Italy, France, Germany, Iran and UK excluding China and separately over India. The corresponding Hurst exponents have been estimated for all these eight different time series. The study shows strong long memory in the cases for USA, Spain and Iran. Again, for Italy, Germany, UK and India the results exhibit feeble long memory with Hurst exponents not significantly far from the critical value of true randomness. On the contrary for France the result shows a very interesting value of Hurst exponent at very near neighbourhood of zero showing a possible short memory in the corresponding data.