Daily New Cases Of COVID-19 Research Articles

Co-movement between COVID-19, oil price and American stock market during 2020: Fresh investigation from partial and multiple wavelet methods

The pandemic has brought economic and social challenges to society, policymakers, financial market investors and firms. The current study aims to shed light on the financial aspects of COVID-19 during the first wave in 2020. In doing so, the study aims to explore the association between COVID-19 cases, oil prices, and the stock market returns (Standard & Poor's). In this paper, the authors use the daily new cases of COVID-19, oil price and stock market return from 21 January 2020 to 10 April 2020. To check the nexus between studied factors, we apply Wavelet Coherence, Partial Wavelet Coherence and Multiple Wavelet Coherence methods. The graphical empirics of Wavelet Coherence Methods mentioned interesting outcomes; (i) COVID-19 and oil price tend to move in the same direction, with COVID-19 leading, (ii) COVID-19 affect stock market returns, and (iii) oil price and stock market returns have strong co-movement.

Non-linear effects of meteorological factors on COVID-19: An analysis of 440 counties in the americas

BackgroundIn the last three years, COVID-19 has caused significant harm to both human health and economic stability. Analyzing the causes and mechanisms of COVID-19 has significant theoretical and practical implications for its prevention and mitigation. The role of meteorological factors in the transmission of COVID-19 is crucial, yet their relationship remains a subject of intense debate. MethodsTo mitigate the issues arising from short time series, large study units, unrepresentative data and linear research methods in previous studies, this study used counties or districts with populations exceeding 100,000 or 500,000 as the study unit. The commencement of local outbreaks was determined by exceeding 100 cumulative confirmed cases. Pearson correlation analysis, generalized additive model (GAM) and distributed lag nonlinear model (DLNM) were used to analyze the relationship and lag effect between the daily new cases of COVID-19 and meteorological factors (temperature, relative humidity, solar radiation, surface pressure, precipitation, wind speed) across 440 counties or districts in seven countries of the Americas, spanning from January 1, 2020, to December 31, 2021. ResultsThe linear correlations between daily new cases and meteorological indicators such as air temperature, relative humidity and solar radiation were not significant. However, the non-linear correlations were significant. The turning points in the relationship for temperature, relative humidity and solar radiation were 5 °C and 23 °C, 74 % and 750 kJ/m2, respectively. ConclusionThe influence of meteorological factors on COVID-19 is non-linear. There are two thresholds in the relationship with temperature: 5 °C and 23 °C. Below 5 °C and above 23 °C, there is a positive correlation, while between 5 °C and 23 °C, the correlation is negative. Relative humidity and solar radiation show negative correlations, but there is a change in slope at about 74 % and 750 kJ/m2, respectively.

An extreme value analysis of daily new cases of COVID-19 for sixteen countries in west Africa

We provide an extreme value analysis of daily new cases of COVID-19. We use data from Benin, Burkina Faso, Cabo Verde, Cote d’Ivoire, Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone and Togo, covering a period of 37 months. Extreme values were defined as monthly maximums of daily new cases. The generalized extreme value distribution was fitted to them with two of its three parameters allowed to vary linearly or quadratically with respect to month number. Ten of the sixteen countries were found to exhibit significant downward trends in monthly maximums. The adequacy of fits was assessed by probability plots and the Kolmogorov-Smirnov test. The fitted models were used to derive quantiles of the monthly maximum of new cases as well as their limits when the month number is taken to infinity.

Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis.

This study explored the influence of daily new case videos posted by public health agencies (PHAs) on TikTok in the context of COVID-19 normalization, as well as public sentiment and concerns. Five different stages were used, based on the Crisis and Emergency Risk Communication model, amidst the 2022 Shanghai lockdown. After dividing the duration of the 2022 Shanghai lockdown into stages, we crawled all the user comments of videos posted by Healthy China on TikTok with the theme of daily new cases based on these five stages. Third, we constructed the pre-training model, ERNIE, to classify the sentiment of user comments. Finally, we performed semantic network analyses based on the sentiment classification results. First, the high cost of fighting the epidemic during the 2022 Shanghai lockdown was why ordinary people were reluctant to cooperate with the anti-epidemic policy in the pre-crisis stage. Second, Shanghai unilaterally revised the definition of asymptomatic patients led to an escalation of risk levels and control conditions in other regions, ultimately affecting the lives and work of ordinary people in the area during the initial event stage. Third, the public reported specific details that affected their lives due to the long-term resistance to the epidemic in the maintenance stage. Fourth, the public became bored with videos regarding daily new cases in the resolution stage. Finally, the main reason for the negative public sentiment was that the local government did not follow the central government's anti-epidemic policy. Our results suggest that the methodology used in this study is feasible. Furthermore, our findings will help the Chinese government or PHAs improve the possible behaviors that displease the public in the anti-epidemic process.

The non-linear and interactive effects of meteorological factors on the transmission of COVID-19: A panel smooth transition regression model for cities across the globe

The ongoing pandemic created by COVID-19 has co-existed with humans for some time now, thus resulting in unprecedented disease burden. Previous studies have demonstrated the non-linear and single effects of meteorological factors on viral transmission and have a question of how to exclude the influence of unrelated confounding factors on the relationship. However, the interactions involved in such relationships remain unclear under complex weather conditions. Here, we used a panel smooth transition regression (PSTR) model to investigate the non-linear interactive impact of meteorological factors on daily new cases of COVID-19 based on a panel dataset of 58 global cities observed between Jul 1, 2020 and Jan 13, 2022. This new approach offers a possibility of assessing interactive effects of meteorological factors on daily new cases and uses fixed effects to control other unrelated confounding factors in a panel of cities. Our findings revealed that an optimal temperature range (0°C–20 °C) for the spread of COVID-19. The effect of RH (relative humidity) and DTR (diurnal temperature range) on infection became less positive (coefficient: 0.0427 to −0.0142; p < 0.05) and negative (coefficient: −0.0496 to −0.0248; p < 0.05) with increasing average temperature(T). The highest risk of infection occurred when the temperature was −10 °C and RH was >80% or when the temperature was 10 °C and DTR was 1 °C. Our findings highlight useful implications for policymakers and the general public.

Use of interrupted Time Series Design in COVID-19 data of Nepal

Introduction: Nepal was hit hard by COVID-19 pandemic and is still struggling with its implications for individuals, societies, and health systems. We applied Interrupted Time Series (ITS) analysis to see if the government's decision to end two nationwide lockdowns had an impact on the average daily new cases of COVID-19 reported in Nepal.&#x0D; &#x0D; Method: ITS analysis on daily new cases of COVID-19 was performed for first and second nationwide lockdown on data of Nepal from data depository for 2019 Novel Coronavirus Visual Dashboard operated by Johns Hopkins University Center for Systems Science and Engineering. Impact of ''ending nationwide lockdown'' and possible associated factors were investigated.&#x0D; Result: The impact of removing first nationwide lockdown contributed to statistically significant rise of an average difference of 899 daily new cases due to community transmission of COVID-19. During second lockdown, even after imposing lockdown, cases overwhelmed existing healthcare system due to high infectivity of new Delta variant followed by a lack of adherence to public health measures and delayed decision to impose lockdown due to economic reasons. Whereas significant decline of cases was observed with an average difference of 2970 daily new cases after ending second nationwide lockdown mostly due to vaccine acceptance.&#x0D; &#x0D; Conclusion: First lockdown was imposed early due to uncertainty about progression of disease, curative measures and unavailability of vaccines. Ending of first lockdown was done untimely due to economic reasons and festive seasons. The unusual decline in cases after ending second lockdown could be contributed to increased vaccination in the country.&#x0D;

Modelling and forecasting new cases of Covid-19 in Nigeria: Comparison of regression, ARIMA and machine learning models

Covid-19 remains a global pandemic threatening hundreds of countries in the world. The impact of Covid-19 has been felt in almost every aspect of life and it has introduced globally, a new normal of livelihood. This global pandemic has triggered unparalleled global health and economic crisis. Therefore, modelling and forecasting the dynamics of this pandemic is very crucial as it will help in decision making and strategic planning. Nigeria as the most populous country in Africa and most populous black nation in the world has been adversely affected by Covid-19 pandemic. This study models and compares forecasting performance of regression, ARIMA and Machine Learning models in predicting new cases of Covid-19 in Nigeria. The study obtained data on daily new cases of Covid-19 in Nigeria between 27th February, 2020 and 30th November, 2021. Graphical analysis showed that Nigeria had witnessed three waves of Covid-19 with the first wave between 27th February, 2020 and 23rd October, 2020, the second wave between 24th October, 2020 and 20th June, 2021 and the third wave between 21st June, 2021 and 30th November, 2021.The second wave recorded the highest spikes in new cases compared to the first wave and third wave. Result reveals that in terms of forecasting performance, inverse regression model outperformed other regression models considered as it shows lowest RMSE of 0.4130 compared with other regression models. Also, the ARIMA (4, 1, 4) outperformed other ARIMA models as it reveals the highest R2 of 0.856 (85.6%), least RMSE (0.6364), AIC (-8.6024) and BIC (-8.5299). Result reveals that Fine tree which is one of the Machine Learning models is more reliable in forecasting new cases of Covid-19 in Nigeria compared to other models as Fine tree gave the highest R2 of 0.90 (90.0%) and least RMSE of 0.22165. Result of 15 days forecasting indicates that Covid-19 pandemic is not over yet in Nigeria as new cases of Covid-19 is projected to increase on 15/12/2021 with predicted new cases of 988 compared with that of 14/12/2021, where only 729 new cases was predicted. This therefore emphasizes the need to strengthen and maintain the existing Covid-19 preventive measures in Nigeria.

Investigating Linkages Between Spatiotemporal Patterns of the COVID-19 Delta Variant and Public Health Interventions in Southeast Asia: Prospective Space-Time Scan Statistical Analysis Method.

BackgroundThe COVID-19 Delta variant has presented an unprecedented challenge to countries in Southeast Asia (SEA). Its transmission has shown spatial heterogeneity in SEA after countries have adopted different public health interventions during the process. Hence, it is crucial for public health authorities to discover potential linkages between epidemic progression and corresponding interventions such that collective and coordinated control measurements can be designed to increase their effectiveness at reducing transmission in SEA.ObjectiveThe purpose of this study is to explore potential linkages between the spatiotemporal progression of the COVID-19 Delta variant and nonpharmaceutical intervention (NPI) measures in SEA. We detected the space-time clusters of outbreaks of COVID-19 and analyzed how the NPI measures relate to the propagation of COVID-19.MethodsWe collected district-level daily new cases of COVID-19 from June 1 to October 31, 2021, and district-level population data in SEA. We adopted prospective space-time scan statistics to identify the space-time clusters. Using cumulative prospective space-time scan statistics, we further identified variations of relative risk (RR) across each district at a half-month interval and their potential public health intervention linkages.ResultsWe found 7 high-risk clusters (clusters 1-7) of COVID-19 transmission in Malaysia, the Philippines, Thailand, Vietnam, and Indonesia between June and August, 2021, with an RR of 5.45 (P<.001), 3.50 (P<.001), 2.30 (P<.001), 1.36 (P<.001), 5.62 (P<.001), 2.38 (P<.001), 3.45 (P<.001), respectively. There were 34 provinces in Indonesia that have successfully mitigated the risk of COVID-19, with a decreasing range between –0.05 and –1.46 due to the assistance of continuous restrictions. However, 58.6% of districts in Malaysia, Singapore, Thailand, and the Philippines saw an increase in the infection risk, which is aligned with their loosened restrictions. Continuous strict interventions were effective in mitigating COVID-19, while relaxing restrictions may exacerbate the propagation risk of this epidemic.ConclusionsThe analyses of space-time clusters and RRs of districts benefit public health authorities with continuous surveillance of COVID-19 dynamics using real-time data. International coordination with more synchronized interventions amidst all SEA countries may play a key role in mitigating the progression of COVID-19.

CausalGNN: Causal-Based Graph Neural Networks for Spatio-Temporal Epidemic Forecasting

Infectious disease forecasting has been a key focus in the recent past owing to the COVID-19 pandemic and has proved to be an important tool in controlling the pandemic. With the advent of reliable spatiotemporal data, graph neural network models have been able to successfully model the inter-relation between the cross-region signals to produce quality forecasts, but like most deep-learning models they do not explicitly incorporate the underlying causal mechanisms. In this work, we employ a causal mechanistic model to guide the learning of the graph embeddings and propose a novel learning framework -- Causal-based Graph Neural Network (CausalGNN) that learns spatiotemporal embedding in a latent space where graph input features and epidemiological context are combined via a mutually learning mechanism using graph-based non-linear transformations. We design an attention-based dynamic GNN module to capture spatial and temporal disease dynamics. A causal module is added to the framework to provide epidemiological context for node embedding via ordinary differential equations. Extensive experiments on forecasting daily new cases of COVID-19 at global, US state, and US county levels show that the proposed method outperforms a broad range of baselines. The learned model which incorporates epidemiological context organizes the embedding in an efficient way by keeping the parameter size small leading to robust and accurate forecasting performance across various datasets.

Impact of the COVID-19 pandemic on travel behavior: A case study of domestic inbound travelers in Jeju, Korea.

This study analyzes a large-scale navigation dataset that captures travel activities of domestic inbound visitors in Jeju, Korea in the first nine months of 2020. A collection of regression models are introduced to quantify the dynamic effects of local and national COVID-19 indicators on their travel behavior. Results suggest that behavior of inbound travelers was jointly affected by pandemic severity locally and remotely. The daily number of new cases in Jeju has a greater impact on reducing travel activities than the national-level daily new cases of COVID-19. The impacts of the pandemic did not diminish over time but produced heterogeneous effects on travels with different trip purposes. Our findings reveal the persistence of COVID-19's effects on travel behavior and the variability in travelers' responses across tourism activities with different levels of perceived health risks. The implications for crisis management and recovery strategies are also discussed.

Dynamics of COVID-19 in the Gulf Cooperation Council (GCC) countries

ObjectivesThe cumulative numbers of confirmed cases, despite providing few details regarding the dynamics, are widely used to model the COVID-19 pandemic. The purpose of this study was to determine the dynamics of COVID-19 in the Gulf Cooperation Council (GCC) countries by using the number of daily new cases rather than the cumulative number of new cases. MethodsData on daily new cases of COVID-19 in the GCC countries from February 2020 to September 2021 were obtained from the Worldometer website. In MATLAB, the Savitzky–Golay filter was used to obtain smoothed curves of the daily profiles of the pandemic, and power spectrum analysis was performed to identify the dominant frequencies. ResultsThe smoothed curves indicated that the GCC countries have experienced two major waves of the pandemic with different peaks and durations. During the first wave, the exponential growth rates ranged from 9 cases/day in Bahrain to 53 cases/day in KSA, whereas the decline rates varied from 6 cases/day in Kuwait to 72 cases/day in KSA. ConclusionsDespite the similarities in socio-economic and environmental conditions among GCC countries, the results indicated that the dynamics of COVID-19 are unique for each GCC country.

One Year of COVID-19 Infection in Iraq and EMR Countries

Background : COVID-19 pandemic affected the world for the last 20 months (January 2020-August 2021. It was troublesome, devestating in terms of cases, deaths and socioeconomic consequences and unpredictable in behaviour.&#x0D; Objectives : To document comparative profile of the COVID -19 in Iraq and other Eastern Mediterranean Countries in terms of time trend and three epidemiological parameters ( incidence, case fatality and mortality rates).&#x0D; Methods : A record-based prospective study. Data were checked daily throughout the study period from February 21, 2020 through February 23, 2021. The targeted numbers were recorded on an excel sheet or word table and updated daily over the specified period. The data covered the period from the onset of the pandemic in various countries. Data included the daily new cases of COVID-19 reported by various government agencies and Ministries of Health in the region supported by two private websites. The multiple sources used assured cross-matching of reported figures and a very high degree of consistency was achieved. &#x0D; Results: The annual incidence rate, case fatality ratio and cause specific mortality rate in Iraq were 17,636.8 per million, 1.97% and 347.3 per million in that order. These three rates were very variable among various Eastern Mediterranean Countries. &#x0D; Conclusion : Iraq and other Countries in the region were harshly struck by COVID-19. Extensive variation in the major epidemiological parameters were documented. The pandemic uncovered major defects in the health care systems and still awaiting a hope to contain this unpredictable event.

Stochastic forecasting of COVID-19 daily new cases across countries with a novel hybrid time series model.

An unprecedented outbreak of the novel coronavirus (COVID-19) in the form of peculiar pneumonia has spread globally since its first case in Wuhan province, China, in December 2019. Soon after, the infected cases and mortality increased rapidly. The future of the pandemic’s progress was uncertain, and thus, predicting it became crucial for public health researchers. These predictions help the effective allocation of health-care resources, stockpiling, and help in strategic planning for clinicians, government authorities, and public health policymakers after understanding the extent of the effect. The main objective of this paper is to develop a hybrid forecasting model that can generate real-time out-of-sample forecasts of COVID-19 outbreaks for five profoundly affected countries, namely the USA, Brazil, India, the UK, and Canada. A novel hybrid approach based on the Theta method and autoregressive neural network (ARNN) model, named Theta-ARNN (TARNN) model, is developed. Daily new cases of COVID-19 are nonlinear, non-stationary, and volatile; thus, a single specific model cannot be ideal for future prediction of the pandemic. However, the newly introduced hybrid forecasting model with an acceptable prediction error rate can help healthcare and government for effective planning and resource allocation. The proposed method outperforms traditional univariate and hybrid forecasting models for the test datasets on an average.

Impact of COVID-19 on pornography use: Evidence from big data analyses.

IntroductionCoronavirus disease 2019 (COVID-19) has led to radical changes in social distancing awareness and affected social relationships. Owing to large-scale lockdown, home quarantine and social distancing requirements, it was anticipated that sexual activities would be severely impacted. However, retrospective self-report studies showed that pornography use and autoerotism increased during the pandemic.AimThis study used big-data databases available on the Internet to investigate factors that modulated pornography use during the pandemic.MethodsDaily relative search volume (RSV) data from Google Trends for the period from 24 February 2020 to 13 July 2020 were extracted. Pornhub traffic data were extracted from the Pornhub Insights website, for the period from 24 February 2020 to 13 July 2020. The parameter was defined as ‘percent change in traffic compared to an average day in 2019’. The number of daily new cases of COVID-19 was extracted from the database on Our World in Data.Outcome measuresThe normality of the data was examined using the Shapiro-Wilk test. All variables included in this study were non-normally distributed. Therefore, non-parametric tests or parametric tests with bootstrapping were adopted where appropriate.ResultsAccording to Google Trends, the RSV for ‘pornography’ increased after late March 2020, which is close to the date when the World Health Organization declared COVID-19 a global pandemic. The number of daily new cases of COVID-19 was positively correlated with the traffic of Pornhub, a popular pornography website, and the RSV for ‘pornography’. Moderation analysis demonstrated a significant main effect of daily new cases of COVID-19 and the RSV for ‘social distancing’ in predicting Pornhub traffic/RSV for ‘pornography’. Furthermore, the RSV for ‘social distancing’ significantly moderated the relationship between daily new cases and Pornhub traffic/RSV for ‘pornography’. A stronger COVID-pornography use association was observed with increased social distancing awareness.ConclusionIncreased pornography consumption during the pandemic was observed, and it was associated with the severity of the pandemic. Social distancing awareness could be a key factor influencing interest in and use of pornography. Further studies on the changes in sexual desire and birth-rate control are worthwhile because long-term public health may be affected by the changes in sexual behaviour during the pandemic.

Effects of temperature and relative humidity on the COVID-19 pandemic in different climates: a study across some regions in Algeria (North Africa).

After more than a year from the first confirmed cases of coronavirus (COVID-19) disease, the role of meteorological factors in the transmission of the virus still needs to be correctly determined. In this scenario of deep uncertainty, the present study aims to investigate the effects of temperature and relative humidity on daily new cases of COVID-19. For this purpose, the COVID-19’s development of infection in fourteen Algerian cities characterized by different climatic conditions, during the period from April 1, 2020, to August 31, 2020, has been investigated. A detailed time series analysis along with linear regression was used to state a possible correlation among some climate’s factor variability (temperature and relative humidity) and daily new confirmed cases of COVID-19. The results showed a weak correlation between daily new cases of COVID-19 and meteorological factors throughout the selected regions. In addition, we concluded that the COVID-19 could fit to high or low values of temperature and relative humidity, and other factors not climates could affect the spreading of the virus like demography and human contact. So, after the discovery of the vaccine and before vaccination of 70% of the world’s population, living with the virus has become an inevitable reality, and it is mandatory to apply the sanitary procedures to slow down the COVID-19 transmission.Graphical abstract

Emerging application of Google Trends searches on "conjunctivitis" for tracing the course of COVID-19 pandemic.

Purpose:The aim of the present study was to use Google Trends for evaluating the association between the internet searches of the term “conjunctivitis” and the daily new cases of COVID-19.Methods:The relative search volume (RSV) of conjunctivitis from January 1 to April 16, 2019 (control group), January 1 to April 16, 2020 (first wave), and October 1 to December 31, 2020 (second wave) was obtained using Google Trends in Italy, France, United Kingdom, and United States. The number of COVID-19 daily new cases in the same countries were retrieved from Worldometer. Lag time correlation analyses were performed to evaluate the relationship between RSV and daily new cases (Pearson’s correlation coefficient).Results:During the first wave, the lagged RSV of conjunctivitis was significantly correlated with the number of COVID-19 daily new cases in all investigated countries. The highest correlation coefficients were obtained with a lag of 16 days in Italy (R = 0.868), 18 days in France (R = 0.491), 15 days in United Kingdom (R = 0.883), and 14 days in United States (R = 0.484) (all p < 0.001). Conversely, no significant correlations were found in the second wave and in the control group.Conclusion:Google Trends searches on conjunctivitis were significantly correlated with COVID-19 daily new cases during the first wave in Italy, France, United Kingdom, and United States, with a lag of 14–18 days. Repeating the analysis for the second wave, however, no significant correlations were found in any of the investigated countries.

A study on the effects of meteorological and climatic factors on the COVID-19 spread in Canada during 2020.

Graphical abstractThe Coronavirus (COVID-19) pandemic has infected more than three million people, with thousands of deaths and millions of people into quarantine. In this research, the authors focus on meteorological and climatic factors on the COVID-19 spread, the main parameters including daily new cases of COVID-19, carbon dioxide (CO2) emission, nitrogen dioxide (NO2), Sulfur dioxide (SO2), PM2.5, Ozone (O3), average temperature, and humidity are examined to understand how different meteorological parameters affect the COVID-19 spread in Canada? The graphical quantitative analysis results indicate that CO2 emissions, air quality, temperature, and humidity have a direct negative relationship with COVID-19 infections. Quantile regression analysis revealed that air quality, Nitrogen, and Ozone significantly induce the COVID-19 spread across Canadian provinces. The findings of this study are contrary to the earlier studies, which argued that weather and climate change significantly increase COVID-19 infections. We suggested that meteorological and climatic factors might be critical to reducing the COVID-19 new cases in Canada based on the findings. This work’s empirical conclusions can provide a guideline for future research and policymaking to stop the COVID-19 spread across Canadian provinces.

The Causality Inference of Public Interest in Restaurants and Bars on Daily COVID-19 Cases in the United States: Google Trends Analysis.

BackgroundThe COVID-19 pandemic has affected virtually every region in the world. At the time of this study, the number of daily new cases in the United States was greater than that in any other country, and the trend was increasing in most states. Google Trends provides data regarding public interest in various topics during different periods. Analyzing these trends using data mining methods may provide useful insights and observations regarding the COVID-19 outbreak.ObjectiveThe objective of this study is to consider the predictive ability of different search terms not directly related to COVID-19 with regard to the increase of daily cases in the United States. In particular, we are concerned with searches related to dine-in restaurants and bars. Data were obtained from the Google Trends application programming interface and the COVID-19 Tracking Project.MethodsTo test the causation of one time series on another, we used the Granger causality test. We considered the causation of two different search query trends related to dine-in restaurants and bars on daily positive cases in the US states and territories with the 10 highest and 10 lowest numbers of daily new cases of COVID-19. In addition, we used Pearson correlations to measure the linear relationships between different trends.ResultsOur results showed that for states and territories with higher numbers of daily cases, the historical trends in search queries related to bars and restaurants, which mainly occurred after reopening, significantly affected the number of daily new cases on average. California, for example, showed the most searches for restaurants on June 7, 2020; this affected the number of new cases within two weeks after the peak, with a P value of .004 for the Granger causality test.ConclusionsAlthough a limited number of search queries were considered, Google search trends for restaurants and bars showed a significant effect on daily new cases in US states and territories with higher numbers of daily new cases. We showed that these influential search trends can be used to provide additional information for prediction tasks regarding new cases in each region. These predictions can help health care leaders manage and control the impact of the COVID-19 outbreak on society and prepare for its outcomes.

Inconsistencies in countries COVID-19 data revealed by Benford’s law

Reporting of daily new cases and deaths on COVID-19 is one of the main tools to understand and menage the pandemic. However, governments and health authorities worldwide present divergent procedures while registering and reporting their data. Most of the bias in those procedures are influenced by economic and political pressures and may lead to intentional or unintentional data corruption, what can mask crucial information. Benford’s law is a statistical phenomenon, extensively used to detect data corruption in large data sets. Here, we used the Benford’s law to screen and detect inconsistencies in data on daily new cases of COVID-19 reported by 80 countries. Data from 26 countries display severe nonconformity to the Benford’s law (p&lt; 0.01), what may suggest data corruption or manipulation.

Human Mobility Patterns and Its Cross-Correlation with the COVID-19 Transmission in Jakarta, Indonesia

The novel coronavirus disease (COVID-19) has been rapidly spreading and causing a severe health crisis in Indonesia. In the absence of medically proven vaccines, official authorities commonly adopt a social control approach under the strict public order. The Indonesian Government is no different, which imposed a large scale social restriction as an intervention strategy in Jakarta and other major cities. In this paper, we aim to quantify the correlation between human mobility and the daily new cases of COVID-19 which are further divided into two main distinct patterns: weekdays and weekends. We utilized the big data sources presented by reputable online service providers include Apple, Waze, and Google in terms of the reported daily mobility patterns. The mobility patterns include driving and walking activities as well as the movement trends across different public places such as retail and recreation, pharmacies, groceries, parks, etc from March 1 to July 31, 2020. To capture the real growth rate of COVID-19, we use the official data reported by Governmental Disease Response Acceleration Task Force. Our cross-correlation analysis revealed that in Jakarta (province-level) and Indonesia (country-level), the COVID-19 case daily growth rate is correlated with the human mobility patterns of driving and walking activities on both weekends and weekdays time. Our work put the additional insight in supporting the importance of social distancing as an effective mitigation strategy to combat the coronavirus outbreak.