Daily Number Of Deaths Research Articles

The first 2 years of COVID-19 in Italy: Incidence, lethality, and health policies.

The novel coronavirus disease 2019 (COVID-19) is an ongoing pandemic that was first recognized in China in December 2019. This paper aims to provide a detailed overview of the first 2 years of the pandemic in Italy. Using the negative binomial distribution, the daily incidence of infections was estimated through the virus's lethality and the moving-averaged deaths. The lethality of the original strain (estimated through national sero-surveys) was adjusted daily for age of infections, hazard ratios of virus variants, and the cumulative distribution of vaccinated individuals. From February 24, 2020, to February 28, 2022, there were 20,833,018 (20,728,924-20,937,375) cases distributed over five waves. The overall lethality rate was 0.73%, but daily it ranged from 2.78% (in the first wave) to 0.15% (in the last wave). The first two waves had the highest number of daily deaths (about 710) and the last wave showed the highest peak of daily infections (220,487). Restriction measures of population mobility strongly slowed the viral spread. During the 2nd year of the pandemic, vaccines prevented 10,000,000 infections and 115,000 deaths. Almost 40% of COVID-19 infections have gone undetected and they were mostly concentrated in the first year of the pandemic. From the second year, a massive test campaign made it possible to detect more asymptomatic cases, especially among the youngest. Mobility restriction measures were an effective suppression strategy while distance learning and smart working were effective mitigation strategies. Despite the variants of concern, vaccines strongly reduced the pandemic impact on the healthcare system avoiding strong restriction measures.

Excess mortality and protected areas during the COVID-19 pandemic: Evidence from Italian municipalities

There is widespread debate on the drivers of heterogeneity of adverse COVID-19 pandemic outcomes and, more specifically, on the role played by context-specific factors. We contribute to this literature by testing the role of environmental factors as measured by environmentally protected areas. We test our research hypothesis by showing that the difference between the number of daily deaths per 1,000 inhabitants in 2020 and the 2018–19 average during the pandemic period is significantly lower in Italian municipalities located in environmentally protected areas such as national parks, regional parks, or Environmentally Protected Zones. After controlling for fixed effects and various concurring factors, municipalities with higher share of environmentally protected areas show significantly lower mortality during the pandemic than municipalities that do not benefit from such environmental amenities.

Estimating the risk of SARS-CoV-2 deaths using a Markov switching-volatility model combined with heavy-tailed distributions for South Africa

BackgroundSARS-CoV-2 (Covid-19 virus) infection exposed the unpreparedness of African countries to health-related issues, South Africa included. Africa recorded more than 211 853 deaths as a consequence of Covid-19. When rare and deadly diseases require urgent hospitalisation strikes, governments and healthcare providers are usually caught unprepared, resulting in huge loss of lives. Usually, at the beginning of such pandemics, there is no rich data for health practitioners and academics to be able to forecast the number of patients or deaths related to the pandemic. This study aims to predict the number of deaths associated with Covid-19 infection. With the availability of the number of deaths on a daily basis, the results stemming from this study are important to inform and plan health policy.MethodsThis study uses the daily number of deaths due to Covid-19 infection. Exploratory data analysis reveals that the data exhibits non-normality, three structural breaks and volatility clustering characteristics. The Markov switching (MS)-generalized autoregressive conditional heteroscedasticity (GARCH)-type model combined with heavy-tailed distributions is fitted to the returns of the data. Using available daily reported Covid-19-related deaths up until 26 August 2021, we report 10-day ahead forecasts of deaths. All forecasts are compared to the actual observed values in the forecasting period.ResultsThe Anderson–Darling Goodness of fit test confirms that the fitted models are adequate for the data. The Kupiec likelihood ratio test and the root mean square error (RMSE) were used to select the robust model at different risk levels. At 95% the MS(3)-GARCH(1,1) combined with Pearson’s type IV distribution (PIVD) is the best model. This indicates that the proposed best-fitting model is reasonable and can be used for predicting the daily number of deaths due to Covid-19.ConclusionThe MS(3)-GARCH(1,1)-PIVD model provides a reliable and accurate method for predicting the minimum number of death due to Covid-19. The accuracy of the proposed model will assist policymakers, academics and health practitioners in forecasting the volatility of future health-related deaths in which the predictability of volatility plays an integral role in health risk management.

Heatwaves and forest fires and their combined effects in the Greater Athens area

Background: Previous severe heatwaves in Athens were associated with excess mortality. As example, the heatwave in July 1987 led to 100% increase in the monthly number of deaths. Also, the occurrence of forest fires burning more than 30km2 of land are associated with increased mortality (>90% increase in respiratory deaths). The aim of the present work is to quantify the effects of a heatwave combined with a forest fire event in July-August 2021. Methods: Data for weekly and daily temperature, particulate air pollution and number of deaths were recorded for the summer 2021 and the 10 previous years. For the 2020-21 period the daily number of deaths due to COVID-19 was also available. A Poisson regression analysis was applied to estimate the effect of the heatwave and forest fire episode on mortality. A comparison with what would be expected in terms of excess deaths, if the same associations observed in the 1987 heatwave were still applicable, was undertaken. Results: PM2.5 concentrations were increased to 73μg/m3 on the 9th heatwave day (2nd forest fire day). The weekly number of deaths increased up to 80% compared to the observed number of deaths during the same time period in 2017-2020. However, the observed impact in 2021 was smaller to what would be expected based on projections using exposure-response functions estimated for the 1987 heat wave. The increase in mortality during the 2021 heatwave could not be explained by an increase in COVID-19 related deaths. Conclusions: The impact of the 2021 heatwave–forest fire event on the number of deaths for the Athens population was very severe. However, it was less than expected based on observations from an important heatwave in the same period in 1987. The difference is probably due to raised awareness and behavioral adaptation. Key words: heatwave, forest fire, mortality, Athens

Factors influencing the COVID-19 pandemic situation in Indonesia, Malaysia and Taiwan in 2021

ObjectivesThe COVID-19 pandemic started over 2 years ago and spread rapidly throughout the world. The total number of cases and deaths is still increasing and the situation remains active across the globe. In the Asian region, COVID-19 vaccination began in early 2021; however, the COVID-19 situation remains uncertain. This study aims to compare the factors that influenced the COVID-19 pandemic situation in three countries in Asia (namely, Indonesia, Malaysia and Taiwan) throughout 2021. Study designThis ecological study utilises the data from the ‘Our World in Data’ website. MethodsIn this study, the COVID-19 pandemic situation in each country is described by looking at the average daily number of deaths and cases per million population throughout 2021. A paired t-test was conducted to compare the significance of differences in the pandemic situation between 2020 and 2021. In addition, the COVID-19 vaccination profiles throughout 2021 were investigated. A multiple linear regression analysis was then performed to develop models to explain the factors influencing the COVID-19 pandemic situation in these three countries. ResultsThe COVID-19 pandemic situation in Indonesia, Malaysia and Taiwan in 2021 is significantly different from 2020. Malaysia had the highest COVID-19 vaccination coverage (79.4%), followed by Taiwan (78.5%) and Indonesia (58.3%). This study found that the following three factors consistently influenced the number of deaths and cases in these three countries [1]: positivity rate [2]; number of tests per 1000 population; and [3] number of tests per case. ConclusionsAlthough the severity of the COVID-19 pandemic situations in Indonesia, Malaysia and Taiwan was different, it is significantly influenced by the quality and quantity of COVID-19 testing and screening, in addition to the vaccination programmes and restriction policies implemented in each country. As a result of the ability of the SARS-CoV-2 virus to mutate, it is recommended that each country strengthen their comprehensive approach to have an effective and efficient coping strategy for the COVID-19 pandemic.

A herd immunity approach to the COVID-19 pandemic?

Viral contamination is one of the most urgent and important topics of environmental pollution. COVID-19 is primarily transmitted from person to person, but can also be transmitted from person to animal. Herd immunity must meet the requirements in order to fulfill the goal of mitigating and ending COVID-19. This paper shows five reasons or conditions why herd immunity is not achieved in the present policies without proposed effective strategies in this paper. Unless one of the five reasons for the herd immunity model is met, the promise of herd immunity will not be fulfilled. Many COVID-19 policies worldwide with current vaccines do not meet the requirements. Policymakers have been relying on unreliable R. The number of daily deaths instead of the number of cases is a good indicator of the pandemic which will be mainly used in this paper. Currently, even in vaccinated countries, resurgences are being observed with new variants with spike mutations and immune escape. This paper proposes an effective multipronged approach such as a pharmacological approach and a non-pharmacological approach including digital fencing. Two tools such as scorecovid and deathdaily were used for justifying the claims. Digital fencing as well as pharmacological approaches may be able to overcome the pandemic. Two tools such as scorecovid and deathdaily showed that the proposed multipronged approach will be effective for mitigating the pandemic.

Designing school reopening in the COVID-19 pre-vaccination period in Bogotá, Colombia: A modeling study.

The COVID-19 pandemic has affected millions of people around the world. In Colombia, 1.65 million cases and 43,495 deaths were reported in 2020. Schools were closed in many places around the world to slow down the spread of SARS-CoV-2. In Bogotá, Colombia, most of the public schools were closed from March 2020 until the end of the year. School closures can exacerbate poverty, particularly in low- and middle-income countries. To reconcile these two priorities in health and fighting poverty, we estimated the impact of school reopening for in-person instruction in 2021. We used an agent-based model of SARS-CoV-2 transmission calibrated to the daily number of deaths. The model includes schools that represent private and public schools in terms of age, enrollment, location, and size. We simulated school reopening at different capacities, assuming a high level of face-mask use, and evaluated the impact on the number of deaths in the city. We also evaluated the impact of reopening schools based on grade and multidimensional poverty index. We found that school at 35% capacity, assuming face-mask adherence at 75% in>8 years of age, had a small impact on the number of deaths reported in the city during a third wave. The increase in deaths was smallest when only pre-kinder was opened, and largest when secondary school was opened. At larger capacities, the impact on the number of deaths of opening pre-kinder was below 10%. In contrast, reopening other grades above 50% capacity substantially increased the number of deaths. Reopening schools based on their multidimensional poverty index resulted in a similar impact, irrespective of the level of poverty of the schools that were reopened. The impact of schools reopening was lower for pre-kinder grades and the magnitude of additional deaths associated with school reopening can be minimized by adjusting capacity in older grades.

Comparison of First and Second Waves of COVID-19 in Metropolitan Chennai City

The study aims to perform a comparative analysis of new cases of COVID-19 reported during the peak time of two waves in Chennai city in 2019-2020.Eight zone-wise variables for 15 zones of Chennai City were studied. They were reduced to fewer factors using a dimension reduction technique called factor analysis, and a standardized index value for each zone and two waves was obtained. The region of hot spots was identified using the Kriging interpolation method using the standardized index value. During wave 1, the average daily number of deaths, new cases, and active cases was 37, 2071, and 21163, respectively, but it was 76, 6663, and 39385 in the second wave. The second wave has a two times higher number of deaths, 3.2 times higher number of new cases, and 1.8 times of active cases. The first wave had a higher percentage of infected people aged 40 years or older, whereas it was more people aged 10-39 years in the second wave. The Krigged estimates showed comparison showed the regions at a higher risk of transmission. Population demographics play a vital role. The COVID-19 zone with a more slum population (Sholiganallur) does not show much intensity of new cases.

Circulatory and Nervous Diseases Mortality Patterns—Comparison of Geomagnetic Storms and Quiet Periods

The aim of this paper is to statistically examine whether there are different patterns in daily numbers of deaths during the quiet periods of solar activity, in contrast to the periods of the strong solar storms. We considered three periods of solar storms (storm of 14 July 2000 Bastille Day Event, storm of 28 October 2003 Halloween Solar Storms, and storm of 17 March 2015 St. Patrick’s Day event) and three periods of continuous very low solar activity (13 September–24 October 1996, 21 July–20 August 2008, and 31 July–31 August 2009) during the Solar Cycles No. 23 and No. 24. In particular, we focus on diseases of the nervous system (group VI from ICD-10) and diseases of the circulatory system (group IX from ICD-10) separately for both sexes and two age groups (under 39 and 40+). We demonstrate that in the resulting graphical models there was a connection between the daily number of deaths and all indices of solar and geomagnetic activity in periods of low solar activity in contrast to periods of strong solar storms in some monitored groups according to age, sex, and group of diagnosis.

Mass Media as a Mirror of the COVID-19 Pandemic

The media plays an important role in disseminating facts and knowledge to the public at critical times, and the COVID-19 pandemic is a good example of such a period. This research is devoted to performing a comparative analysis of the representation of topics connected with the pandemic in the internet media of Kazakhstan and the Russian Federation. The main goal of the research is to propose a method that would make it possible to analyze the correlation between mass media dynamic indicators and the World Health Organization COVID-19 data. In order to solve the task, three approaches related to the representation of mass media dynamics in numerical form—automatically obtained topics, average sentiment, and dynamic indicators—were proposed and applied according to a manually selected list of search queries. The results of the analysis indicate similarities and differences in the ways in which the epidemiological situation is reflected in publications in Russia and in Kazakhstan. In particular, the publication activity in both countries correlates with the absolute indicators, such as the daily number of new infections, and the daily number of deaths. However, mass media tend to ignore the positive rate of confirmed cases and the virus reproduction rate. If we consider strictness of quarantine measures, mass media in Russia show a rather high correlation, while in Kazakhstan, the correlation is much lower. Analysis of search queries revealed that in Kazakhstan the problem of fake news and disinformation is more acute during periods of deterioration of the epidemiological situation, when the level of crime and poverty increase. The novelty of this work is the proposal and implementation of a method that allows the performing of a comparative analysis of objective COVID-19 statistics and several mass media indicators. In addition, it is the first time that such a comparative analysis, between different countries, has been performed on a corpus in a language other than English.

Analysis of the effectiveness of face-coverings on the death ratio of COVID-19 using machine learning

The recent outbreak of the COVID-19 led to death of millions of people worldwide. To stave off the spread of the virus, the authorities in the US employed different strategies, including the mask mandate order issued by the states’ governors. In the current work, we defined a parameter called average death ratio as the monthly average of the number of daily deaths to the monthly average number of daily cases. We utilized survey data to quantify people’s abidance by the mask mandate order. Additionally, we implicitly addressed the extent to which people abide by the mask mandate order, which may depend on some parameters such as population, income, and education level. Using different machine learning classification algorithms, we investigated how the decrease or increase in death ratio for the counties in the US West Coast correlates with the input parameters. The results showed that for the majority of counties, the mask mandate order decreased the death ratio, reflecting the effectiveness of such a preventive measure on the West Coast. Additionally, the changes in the death ratio demonstrated a noticeable correlation with the socio-economic condition of each county. Moreover, the results showed a promising classification accuracy score as high as 90%.

Hourly temperature variability and mortality in 31 major Chinese cities: Effect modification by individual characteristics, season and temperature zone

In the context of ongoing climate change, temperature variability (TV) has been considered as an important trigger of death. However, evidence of association between mortality and hourly temperature variability (HTV) is scarce at the multi-city level, and the time window of health effects of HTV is lack of investigation. This study aims at quantifying the mortality risk and burden of HTV and exploring subpopulations susceptible to HTV from a large-scale multi-city perspective. Data on daily number of deaths and meteorology were collected for 31 Chinese major cities during 2007-2013. HTV was calculated as the standard deviation of hourly temperature within a few days. The optimal exposure period of HTV was chosen according to multiple scientific criteria. A quasi-Poisson regression combined with distributed lag nonlinear model was used to assess the city-specific HTV-mortality associations. Then, meta-analysis was further applied to pool city-specific effect estimates. Finally, we calculated the fraction of mortality attributable to HTV. Stratification analyses were conducted by individual characteristics (i.e. age, sex, and educational attainment), season, and region. HTV calculated in a relatively long-time window like 18 d (HTV0-17) could capture the impact of HTV adequately. Per 1°C raise of HTV0-17 associated with 1.38% (95%CI: 0.77, 1.99) increase of non-accidental mortality. During the study period, 5.47% (95%CI: 1.06, 9.64) of non-accidental mortality could be attributed to HTV. The females, the elderly, and individuals with low education level were more susceptible to HTV than their counterparts, respectively. Moreover, a stronger HTV-mortality association was observed in individuals who live in warmer season and temperature zone. HTV is associated with a considerable mortality burden, which may be modified by season, geographic and individual-level factors. Our findings highlight the practical importance of establishing early warning systems and promoting health education to mitigate the impacts of temperature variability.

COVID-19 in Slovenia, from a Success Story to Disaster: What Lessons Can Be Learned?

During the first wave of the COVID-19 pandemic in spring 2020, Slovenia was among the least affected countries, but the situation became drastically worse during the second wave in autumn 2020 with high numbers of deaths per number of inhabitants, ranking Slovenia among the most affected countries. This was true even though strict non-pharmaceutical interventions (NPIs) to control the progression of the epidemic were being enforced. Using a semi-parametric Bayesian model developed for the purpose of this study, we explore if and how the changes in mobility, their timing and the activation of contact tracing can explain the differences in the epidemic progression of the two waves. To fit the model, we use data on daily numbers of deaths, patients in hospitals, intensive care units, etc., and allow transmission intensity to be affected by contact tracing and mobility (data obtained from Google Mobility Reports). Our results imply that though there is some heterogeneity not explained by mobility levels and contact tracing, implementing interventions at a similar stage as in the first wave would keep the death toll and the health system burden low in the second wave as well. On the other hand, sticking to the same timeline of interventions as observed in the second wave and focusing on enforcing a higher decrease in mobility would not be as beneficial. According to our model, the ‘dance’ strategy, i.e., first allowing the numbers to rise and then implementing strict interventions to make them drop again, has been played at too-late stages of the epidemic. In contrast, a 15–20% reduction of mobility compared to pre-COVID level, if started at the beginning and maintained for the entire duration of the second wave and coupled with contact tracing, could suffice to control the epidemic. A very important factor in this result is the presence of contact tracing; without it, the reduction in mobility needs to be substantially larger. The flexibility of our proposed model allows similar analyses to be conducted for other regions even with slightly different data sources for the progression of the epidemic; the extension to more than two waves is straightforward. The model could help policymakers worldwide to make better decisions in terms of the timing and severity of the adopted NPIs.

Analisis Pengaruh Covid-19 terhadap Indeks Saham Gabungan Indonesia Periode Maret 2020 – Oktober 2020

Abstrak Stock market index is very be affected any issues. In previous research, it was found that several major issues had a significant effect, such as the environment, disasters, political work and pandemic diseases. The increasing transmission of Covid-19 has had a significant influence on stock markets performance in any countries whole world. Purpose of this research is to find impact of Covid-19 pandemic which is daily case confirmations, number of daily deaths, number of daily recoveries and travel bans on Indonesia Stock Exchange during period March 2020 to October 2020. The results show daily case confirmations, the number of recoveries. and travel bans do not have a significant impact on the Indonesia Stock Exchange. While variable number of covid-19 deaths has a significant effect. Keywords: Covid-19; Daily Case; Daily Health; Travel Bans; IHSG

MP34-02 THE IMPACT OF COVID-19 ON CLINIC VOLUME IN A NEW JERSEY COVID-HOTSPOT AT AN ACADEMIC UROLOGY PRACTICE

INTRODUCTION AND OBJECTIVE: COVID-19 has been a disruptive pandemic that has transformed the landscape of the health care system. The pandemic disproportionately affects patients from a racial diverse and economic depressed demographic, highly represented in Newark (Essex County, NJ). We seek to investigate how the COVID-19 pandemic has affected the flow of patients to an inner-city health care system, specifically a major academic urology clinic in Newark, NJ. METHODS: We collected de-identified data for clinic volume and appointment no-show rates from the electronic medical record from September 16, 2019 to January 26, 2021. Publicly available data on COVID cases and death rates in Essex county was collected from public data reporting on March 4, 2020 to January 31, 2021. Data analysis and statistical analysis was performed with Jupyter Notebooks and Microsoft Excel. RESULTS: The number of patients who were no-shows was significantly correlated with daily COVID cases (-0.49, p=0.004). The total number of clinic patients was also less when there were higher COVID cases (-0.41, p=0.02). We found that the rate of no-shows and the daily reported COVID cases was weakly correlated (-0.33, p=0.06). The number of daily deaths from COVID-19 was not statistically correlated with clinic flow. Overall, we found that during the COVID pandemic our no-show rate averaged 25.4±0.093% compared to 25.9±0.065% prior to March 4, 2020. CONCLUSIONS: The total clinic volume and total number of no-show patients was significantly decreased on days with higher reported COVID-19 cases in Essex county. Reported COVID-19 deaths did not correlate with clinic flow. Future research is planned for looking at specific patient demographic data such as race/ ethnicity, language, socioeconomic status, and diagnosis. We will also evaluate whether the no show rate significantly delayed health care for these patients and affected their prognosis.

Optimal Transportation Methods in Nonlinear Filtering

How data became one of the most powerful tools to fight an epidemic is a question that a recent (10 June 2020) The New York Times article poses in its title. Indeed, the spread of COVID-19 involves dynamically evolving hidden data (for example, the number of infected people, the number of asymptomatic people) that must be deduced from noisy and partially observed data (for example, the number of daily deaths, the number of daily hospitalizations, and the number of daily positive tests). The underlying mathematics for posing and solving this and several other partially observed dynamic problems is familiar to control theorists.

Setting the boundaries of COVID-19 lockdown relaxation measures

PurposeThe purpose of this paper is to develop a simple deterministic model that quantifies previously adopted preventive measures driven by the trend of the reported number of deaths in both Italy and India. In addition, the authors forecast the spread based on some selected quantified preventive measures. The optimal exiting policy is derived using the inverse dynamics of the model. Furthermore, the model developed by the authors is dependent on the daily number of deaths; as such, it is sensitive to the death rate but remains insensitive to trends in deaths.Design/methodology/approachIn the wake of COVID-19, policymakers and health professionals realized the limitations and shortcomings of current healthcare systems and pandemic response policies. The need to revise global and national pandemic response mechanisms has been thrust into the public spotlight. To this end, the authors devise an approach to identify the most suitable governmental non-pharmaceutical intervention (NPI) policies, previously adopted in a community, country or region that serve as the foundation for most pandemic strategies.FindingsLeveraging Italy, the authors compare the aftermath by considering three scenarios: (a) recently adopted preventive measures, (b) strictest preventive measures previously adopted, and (c) the optimal exiting policy. In comparison to the second scenario, the authors estimate about twice the number of recoveries and deaths within five months under the first scenario and about 80 times more under the optimal scenario. Whereas in India, the authors applied one scenario of recently adopted preventative measures to showcase the rapid turnaround of their model. According to the new timeline, almost 90% of all deaths in India could have been prevented if the policies implemented in April 2021 were put in place three months prior, i.e. in January 2021.Originality/valueThe novelty of the proposed approach is in the use of inverse dynamics of a simple deterministic model that allows capturing the trend of contact rate as a function of adopted NPIs, regardless of pandemic type.

Do Temperature and Humidity Affect the Transmission of SARS-CoV-2?-A Flexible Regression Analysis.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible virus that causes Coronavirus disease 2019 (COVID-19). Temperature and humidity are two essential factors in the transmission of SARS-CoV-2 affect the respiratory system of human. This study aimed to investigate the effects of temperature and humidity on the transmission of SARS-CoV-2 and the Spread Covid-19. The daily number of SARS-CoV-2 infected new cases, and the number of death due to Covid-19 are considered the response variables. Data are collected from March 08, 2020 to January 31, 2021. A flexible regression model under the Generalized Additive Models for Location Scale and Shape framework is used to analyze data. The temperature and humidity have a significant impact on the transmission of SARS-CoV-2. The temperature is highly significant in the number of SARS-CoV-2 infected new cases and number of death due to COVID-19. In contrast, the humidity is significant on the number of SARS-CoV-2 infected new cases, but it is insignificant on the number of death due to COVID-19 at a 5% level of significance. The analysis revealed that both the temperature and humidity inversely affected the daily number of deaths and new cases of COVID-19.

Population-level interest and trends in meditation and yoga during lockdown imposed due to coronavirus disease 2019 pandemic In India

Introduction: Yoga and meditation have a potential to give mental peace and calm. The present coronavirus disease 2019 (COVID-19) has forced countries to impose lockdown due to its infectious nature, thus restricting people in their homes posing psychosocial impact which can be reduced through yoga. Google Trends (GT) is a proxy indicator for population-level interests, which is used instead of traditional survey methods during pandemic. The objective of this study was to monitor population-level interest and trends in yoga and meditation during lockdown imposed due to COVID-19 in India through GT. Material & Methods: GT is an open-access, web-based tool which provides unfiltered sample of active search requests made to Google. Various keywords related to yoga and meditation were used to retrieve web-based search volume from January 30, 2020, to June 7, 2020, for India. These data were correlated with number of cases and deaths reported due to COVID-19 as an increase in cases and death might lead to stress among masses. Results: The search trends and daily number of confirmed cases were fairly correlated (r = 0.647, P = 0.000). The relative search volume for the search trends was also fairly correlated (r = 0.665, P = 0.000) with number of daily deaths due to COVID-19. States such as Uttarakhand and Goa had a higher share of search whereas Meghalaya and West Bengal searched the least. Conclusion: GT showed an increase in population-level interest in yoga and meditation during COVID-19 lockdown which is a positive indicator for population. This indicates the need for continuity of trend so as to make it a routine habit even after the situation becomes normal.

Impact of Non-pharmaceutical Interventions on the Control of COVID-19 in Iran: A Mathematical Modeling Study.

During the first months of the coronavirus disease 2019 (COVID-19) pandemic, Iran reported high numbers of infections and deaths. In the following months, the burden of this infection decreased significantly, possibly due to the impact of a package of interventions. We modeled the dynamics of COVID-19 infection in Iran to quantify the impacts of these interventions. We used a modified susceptible-exposed-infected-recovered (SEIR) model to model the COVID-19 epidemic in Iran, from January 21, 2020 to September 21, 2020. We estimated the 95% uncertainty intervals (UIs) using Markov chain Monte Carlo simulation. Under different scenarios, we assessed the effectiveness of non-pharmaceutical interventions (NPIs) including physical distancing measures and self-isolation. We also estimated the time-varying reproduction number (Rt ), using our mathematical model and epidemiologic data. If no NPIs were applied, there could have been a cumulative number of 51 800 000 (95% UI: 1 910 000- 77 600 000) COVID-19 infections and 266 000 (95% UI: 119 000-476 000) deaths by September 21, 2020. If physical distancing interventions, such as school/border closures and self-isolation interventions had been introduced a week earlier than they were actually launched, 30.8% and 35.2% reduction in the number of deaths and infections respectively could have been achieved by September 21, 2020. The observed daily number of deaths showed that the Rt was one or more than one almost every day during the analysis period. Our models suggest that the NPIs implemented in Iran between January 21, 2020 and September 21, 2020 had significant effects on the spread of the COVID-19 epidemic. Our study also showed that the timely implementation of NPIs showed a profound effect on further reductions in the numbers of infections and deaths. This highlights the importance of forecasting and early detection of future waves of infection and of the need for effective preparedness and response capabilities.