Cumulative Number Of Cases Research Articles

The epidemiological characteristics of COVID-19 in Libya during the ongoing-armed conflict.

COVID-19 can have even more dire consequences in countries with ongoing armed conflict. Libya, the second largest African country, has been involved in a major conflict since 2011. This study analyzed the epidemiological situation of the COVID-19 pandemic in Libya, examined the impact of the armed conflict in Libya on the spread of the pandemic, and proposes strategies for dealing with the pandemic during this conflict. We collected the available information on all COVID-19 cases in the different regions of Libya, covering the period from March 25th to May 25th 2020. The cumulative number of cases and the daily new cases are presented in a way to illustrate the patterns and trends of COVID-19, and the effect of the ongoing armed conflict was assessed regionally. A total of 698 cases of COVID-19 were reported in Libya within a period of three months. The number of cases varied from one region to another and was affected by the fighting. The largest number of cases were reported in the southern part of the country, which has been severely affected by the conflict in comparison to the eastern and western parts of the country. This study describes the epidemiological pattern of COVID-19 in Libya and how it has been affected by the ongoing-armed conflict. This conflict seems to have hindered access to populations and there by masked he true dimensions of the pandemic. Hence, efforts should be combined to combat these consequences.

The behavior of Sovereign Credit Default Swaps (CDS) spread: evidence from Turkey with the effect of Covid-19 pandemic

This study examines how sovereign CDS spreads of Turkey behave in Covid-19 pandemic times by considering that CDS spreads reflect the riskiness, vulnerability, financial stability, and macroeconomic stability of countries and CDS spreads of most of the emerging countries have increased with the emergence of Covid-19 pandemic. Therefore, the study focuses on the year 2020 which includes before Covid-19 and Covid-19 pandemic times periods. In this context, daily data between 12.06.2019 and 06.16.2020, 6 independent variables, and 6 Covid-19 situations are analyzed by employing Multivariate Adaptive Regression Splines (MARS) method. The findings reveal that (i) influential factors on Turkey's CDS spreads are BIST100 index, VIX index, MSCI Turkey index, and USD/TL foreign exchange rates for the period which is before Covid-19 pandemic times; (ii) MSCI emerging market index, number of new deaths from Covid-19, USD/TL foreign exchange rates, weighted average cost of funds, number of new cases from Covid-19, and VIX index have effect on Turkey's CDS spreads in Covid-19 pandemic times, respectively; (iii) on the other hand, number of cumulative cases, number of cumulative deaths, and measures do not have effect on Turkey's CDS spreads in any period. Taking precautions to decrease negative effects on Turkey's CDS spreads by considering the importance of deaths number from Covid-19 pandemic is very important. Hence, Turkey could stimulate foreign portfolio investment inflows with decreasing CDS spreads.

The Behavior of Sovereign Credit Default Swaps (CDS) Spread: Evidence from Turkey with the Effect of COVID-19 Pandemic

This study examines how sovereign CDS spreads of Turkey behave in COVID-19 pandemic times by considering that CDS spreads reflect the riskiness, vulnerability, financial stability, and macroeconomic stability of countries and CDS spreads of most of the emerging countries have increased with the emergence of COVID-19 pandemic. Therefore, the study focuses on the year 2020 which includes before COVID-19 and COVID-19 pandemic times periods. In this context, daily data between 12.06.2019 and 06.16.2020, 6 independent variables, and 6 COVID-19 situations are analyzed by employing Multivariate Adaptive Regression Splines (MARS) method. The findings reveal that (i) influential factors on Turkey’s CDS spreads are BIST100 index, VIX index, MSCI Turkey index, and USD/TL foreign exchange rates for the period which is before COVID-19 pandemic times; (ii) MSCI emerging market index, number of new deaths from COVID-19, USD/TL foreign exchange rates, weighted average cost of funds, number of new cases from Covid-19, and VIX index have effect on Turkey’s CDS spreads in COVID-19 pandemic times, respectively; (iii) on the other hand, number of cumulative cases, number of cumulative deaths, and measures do not have effect on Turkey’s CDS spreads in any period. Taking precautions to decrease negative effects on Turkey’s CDS spreads by considering the importance of deaths number from COVID-19 pandemic is very important. Hence, Turkey could stimulate foreign portfolio investment inflows with decreasing CDS spreads.

Predicting the Cumulative Number of Cases for the COVID-19 Epidemic in China from Early Data

We model the COVID-19 coronavirus epidemic in China. We use early reported case data to predict the cumulative number of reported cases to a final size. The key features of our model are the timing of implementation of major public policies restricting social movement, the identification and isolation of unreported cases, and the impact of asymptomatic infectious cases.

Predictive Models for Cumulative Confirmed COVID-19 Cases by Day in Southeast Asia

Coronavirus disease 2019 outbreak has spread as a pandemic since the end of year 2019. This situation has been causing a lot of problems of human beings such as economic problems, health problems. The forecasting of the number of infectious people is required by the authorities of all countries including Southeast Asian countries to make a decision and control the outbreak. This research is to investigate the suitable forecasting model for the number of infectious people in Southeast Asian countries. A comparison of forecasting models between logistic growth curve which is symmetric and Gompertz growth curve which is asymmetric based on the maximum of Coefficent of Determination and the minimum of Root Mean Squared Percentage Error is also proposed. The estimation of parameters of the forecasting models is evaluated by the least square method. In addition, spreading of the outbreak is estimated by the derivative of the number of cumulative cases. The findings show that Gompertz growth curve is a suitable forecasting model for Indonesia, Philippines, and Malaysia and logistic growth curve suits the other countries in South Asia.

Predicting the cumulative number of cases for the COVID-19 epidemic in China from early data.

We model the COVID-19 coronavirus epidemic in China. We use early reported case data to predict the cumulative number of reported cases to a final size. The key features of our model are the timing of implementation of major public policies restricting social movement, the identification and isolation of unreported cases, and the impact of asymptomatic infectious cases.

Estimating healthcare resource needs for COVID-19 patients in Nigeria.

Introductioncontinuous assessment of healthcare resources during the COVID-19 pandemic will help in proper planning and to prevent an overwhelming of the Nigerian healthcare system. In this study, we aim to predict the effect of COVID-19 on hospital resources in Nigeria.Methodswe adopted a previously published discrete-time, individual-level, health-state transition model of symptomatic COVID-19 patients to the Nigerian healthcare system and COVID-19 epidemiology in Nigeria by September 2020. We simulated different combined scenarios of epidemic trajectories and acute care capacity. Primary outcomes included the expected cumulative number of cases, days until depletion resources and the number of deaths associated with resource constraints. Outcomes were predicted over a 60-day time horizon.Resultsin our best-case epidemic trajectory, which implies successful implementation of public health measures to control COVID-19 spread, assuming all three resource scenarios, hospital resources would not be expended within the 60-days time horizon. In our worst-case epidemic trajectory, assuming conservative resource scenario, only ventilated ICU beds would be depleted after 39 days and 16 patients were projected to die while waiting for ventilated ICU bed. Acute care resources were only sufficient in the three epidemic trajectory scenarios when combined with a substantial increase in healthcare resources.Conclusionsubstantial increase in hospital resources is required to manage the COVID-19 pandemic in Nigeria, even as the infection growth rate declines. Given Nigeria's limited health resources, it is imperative to focus on maintaining aggressive public health measures as well as increasing hospital resources to reduce COVID-19 transmission further.

A quantitative evaluation of the use of medical lasers in German hospitals.

The laser has become an integral part of modern medicine, procedures based on this technique have found their way into a multitude of medical disciplines. There is, however, no data available on the detailed quantitative development of laser use in the medical sector. This fact gave rise to the idea of the present study, which analyzed the raw data of the quality report of German hospitals with respect to this subject. Over the 9 years of report, a steady increase in the cumulative number of cases was evident, although not all body regions in which the medical laser is used followed this trend. The CO2 laser was found to be the most commonly applied laser, even though a large spectrum of different laser types is used. Based on the present study, the importance of the laser for medical purposes can be confirmed.

Epiflows: an R package for risk assessment of travel-related spread of disease.

As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented.In this paper we present epiflows, an R package for risk assessment of travel-related spread of disease. epiflows produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease.The package also provides tools for geocoding and visualization.We illustrate the use of epiflows by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.

Epiflows: an R package for risk assessment of travel-related spread of disease

As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented. In this paper we present epiflows, an R package for risk assessment of travel-related spread of disease. epiflows produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease. The package also provides tools for geocoding and visualization. We illustrate the use of epiflows by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.

Epiflows: an R package for risk assessment of travel-related spread of disease

As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented. In this paper we present epiflows, an R package for risk assessment of travel-related spread of disease. epiflows produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease. The package also provides tools for geocoding and visualization. We illustrate the use of epiflows by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.

Temporal Trends of Resident Experience in External Beam Radiation Therapy Cases: Analysis of ACGME Case Logs from 2007 to 2018

We sought to characterize temporal trends of radiation oncology resident-reported external beam radiation therapy (EBRT) case experience with respect to various disease sites, including trends in stereotactic radiosurgery and stereotactic body radiation therapy cases. Summarized, deidentified case logs for graduating radiation oncology residents between 2007 and 2018 were obtained from the Accreditation Council for Graduate Medical Education national summary data report. Mean number of cumulative cases and standard deviations per graduating resident by year were evaluated. Cases were subdivided into 12 disease-site categories using the Accreditation Council for Graduate Medical Education classification. Analysis of variance was used to determine significant differences, and strength of association was evaluated using Pearson correlation. The number of graduating residents per year increased by 66% from 114 in 2007 to 189 in 2018 (P < .001, r = 0.88). The overall mean number of EBRT cases per graduating resident decreased by 13.2% from 521.9 in 2007 to 478.5 in 2018, with a decrease in the ratio of nonmetastatic to metastatic cases per graduating resident. There was significant variation among the disease categories analyzed; however, the largest proportionate decreases were seen in hematologic, lung, and genitourinary malignancies. Stereotactic radiosurgery volume per graduating resident increased from an average of 27.9 cases in 2007 to 50.3 in 2018 (P < .001, r = 0.96). Stereotactic body radiation therapy volume per graduating resident increased as well, from a mean of 6 cases in 2007 to 55.6 cases in 2018 (P < .001, r = 0.99). We report a longitudinal summary of resident-reported experience in EBRT cases. These findings have implications for future efforts to optimize residency training programs and requirements.

Systematic biases in disease forecasting – The role of behavior change

In a simple susceptible-infected-recovered (SIR) model, the initial speed at which infected cases increase is indicative of the long-term trajectory of the outbreak. Yet during real-world outbreaks, individuals may modify their behavior and take preventative steps to reduce infection risk. As a consequence, the relationship between the initial rate of spread and the final case count may become tenuous. Here, we evaluate this hypothesis by comparing the dynamics arising from a simple SIR epidemic model with those from a modified SIR model in which individuals reduce contacts as a function of the current or cumulative number of cases. Dynamics with behavior change exhibit significantly reduced final case counts even though the initial speed of disease spread is nearly identical for both of the models. We show that this difference in final size projections depends critically in the behavior change of individuals. These results also provide a rationale for integrating behavior change into iterative forecast models. Hence, we propose to use a Kalman filter to update models with and without behavior change as part of iterative forecasts. When the ground truth outbreak includes behavior change, sequential predictions using a simple SIR model perform poorly despite repeated observations while predictions using the modified SIR model are able to correct for initial forecast errors. These findings highlight the value of incorporating behavior change into baseline epidemic and dynamic forecast models.

Effects of socio-economic and environmental factors on the spatial heterogeneity of dengue fever investigated at a fine scale.

The spatial pattern of dengue fever cases is the result of complex interactions between the virus, the host and the vector, which may be affected by environmental conditions. The largest outbreak of dengue fever in Guangzhou city, China occurred in 2014 with case numbers 2.7 times the number of cumulative cases since 1978 and a significantly non-random spatial distribution. Selecting Guangzhou City as the study area, we used scan statistics to analyze the spatial heterogeneity of dengue fever and a generalized additive model to evaluate and examine the effects of socio-economic and environmental factors on spatial heterogeneity at a fine scale. The study found that the spatial distribution of dengue fever is highly heterogeneous and various factors differ in relative importance. The junction of the central districts of Guangzhou is a high-risk area with the urban village and urban-rural fringe zone formed by urbanization as important regional factors. The low gross domestic product per capita, the high population density, the high road density were perceived as risk factors. The Asian subtropical coastal area together with the socioeconomic and environmental factors were found to be the key drivers at the fine scale explaining the high spatial heterogeneity of dengue fever in Guangzhou City.

Epiflows: an R package for risk assessment of travel-related spread of disease

As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented.In this paper we present epiflows, an R package for risk assessment of travel-related spread of disease.epiflows produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease.The package also provides tools for geocoding and visualization.We illustrate the use of epiflows by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.

Online platform for applying space-time scan statistics for prospectively detecting emerging hot spots of dengue fever.

BackgroundCases of dengue fever have increased in areas of Southeast Asia in recent years. Taiwan hit a record-high 42,856 cases in 2015, with the majority in southern Tainan and Kaohsiung Cities. Leveraging spatial statistics and geo-visualization techniques, we aim to design an online analytical tool for local public health workers to prospectively identify ongoing hot spots of dengue fever weekly at the village level.MethodsA total of 57,516 confirmed cases of dengue fever in 2014 and 2015 were obtained from the Taiwan Centers for Disease Control (TCDC). Incorporating demographic information as covariates with cumulative cases (365 days) in a discrete Poisson model, we iteratively applied space–time scan statistics by SaTScan software to detect the currently active cluster of dengue fever (reported as relative risk) in each village of Tainan and Kaohsiung every week. A village with a relative risk >1 and p value <0.05 was identified as a dengue-epidemic area. Assuming an ongoing transmission might continuously spread for two consecutive weeks, we estimated the sensitivity and specificity for detecting outbreaks by comparing the scan-based classification (dengue-epidemic vs. dengue-free village) with the true cumulative case numbers from the TCDC’s surveillance statistics.ResultsAmong the 1648 villages in Tainan and Kaohsiung, the overall sensitivity for detecting outbreaks increases as case numbers grow in a total of 92 weekly simulations. The specificity for detecting outbreaks behaves inversely, compared to the sensitivity. On average, the mean sensitivity and specificity of 2-week hot spot detection were 0.615 and 0.891 respectively (p value <0.001) for the covariate adjustment model, as the maximum spatial and temporal windows were specified as 50% of the total population at risk and 28 days. Dengue-epidemic villages were visualized and explored in an interactive map.ConclusionsWe designed an online analytical tool for front-line public health workers to prospectively detect ongoing dengue fever transmission on a weekly basis at the village level by using the routine surveillance data.

Trend in cumulative cases and mortality rate among visceral leishmaniasis patients in Eastern Sudan: a 14-year registry, 2002–2015

Leishmaniasis is one of the neglected infectious diseases of the world. This disease largely affects individuals of low socio-economic level, mainly in developing countries. The aim of this study was to investigate the cumulative number of cases, case fatality rate, and trend in incidence and mortality rate of visceral leishmaniasis (VL) in Eastern Sudan. A retrospective descriptive study was performed through analysis of the database managed by the Ministry of Health in Gadarif State, which includes all cases of VL. Over the 14-year study period (2002-2015), a total of 51773 patients were registered in Gadarif State with clinical and laboratory evidence of proven VL. The trend in VL was considerably higher over the years 2003-2005 as compared to the last 5 years of 2011-2015. The highest fatality rate was observed in 2002 (4.8%) and it had declined in 2014 (1.1%) and 2015 (1.7%). Rural residence was statistically associated with death related to VL (p=0.021). This study showed a high number of cumulative cases of VL in Eastern Sudan. The fatality rate was found to be highest among rural residents and has declined in the last 5 years. Thus immediate interventions are needed in terms of health education and the implementation of preventive measures, with a specific focus on people residing in rural areas.

On Temporal Patterns and Circulation of Influenza Virus Strains in Taiwan, 2008-2014: Implications of 2009 pH1N1 Pandemic.

BackgroundIt has been observed that, historically, strains of pandemic influenza led to succeeding seasonal waves, albeit with decidedly different patterns. Recent studies suggest that the 2009 A(H1N1)pdm09 pandemic has had an impact on the circulation patterns of seasonal influenza strains in the post-pandemic years. In this work we aim to investigate this issue and also to compare the relative transmissibility of these waves of differing strains using Taiwan influenza surveillance data before, during and after the pandemic.MethodsWe make use of the Taiwan Center for Disease Control and Prevention influenza surveillance data on laboratory-confirmed subtyping of samples and a mathematical model to determine the waves of circulating (and co-circulating) H1, H3 and B virus strains in Taiwan during 2008–2014; or namely, short before, during and after the 2009 pandemic. We further pinpoint the turning points and relative transmissibility of each wave, in order to ascertain whether any temporal pattern exists.Results/FindingsFor two consecutive years following the 2009 pandemic, A(H1N1)pdm09 circulated in Taiwan (as in most of Northern Hemisphere), sometimes co-circulating with AH3. From the evolution point of view, A(H1N1)pdm09 and AH3 were able to sustain their circulation patterns to the end of 2010. In fact, A(H1N1)pdm09 virus circulated in six separate waves in Taiwan between summer of 2009 and spring of 2014. Since 2009, a wave of A(H1N1)pmd09 occurred every fall/winter influenza season during our study period except 2011–2012 season, when mainly influenza strain B circulated. In comparing transmissibility, while the estimated per capita weekly growth rates for cumulative case numbers (and the reproduction number) seem to be lower for most of the influenza B waves (0.06~0.26; range of 95% CIs: 0.05~0.32) when compared to those of influenza A, the wave of influenza B from week 8 to week 38 of 2010 immediately following the fall/winter wave of 2009 A(H1N1) pdm09 was substantially higher at r = 0.89 (95% CI: 0.49, 1.28), in fact highest among all the waves detected in this study. Moreover, when AH3 or A(H1N1)pdm09 exhibit high incidence, reported cases of subtype B decreases and vice versa. Further modeling analysis indicated that during the study period, Taiwan nearly experienced at least one wave of influenza epidemic of some strain every summer except in 2012.DiscussionEstimates of R for seasonal influenza are consistent with that of temperate and tropical-subtropical regions, while estimate of R for A(H1N1)pdm09 is comparatively less than countries in Europe and North America, but similar to that of tropical-subtropical regions. This offers indication of regional differences in transmissibility of influenza virus that exists only for pandemic influenza. Despite obvious limitations in the data used, this study, designed to qualitatively compare the temporal patterns and transmissibility of the waves of different strains, illustrates how influenza subtyping data can be utilized to explore the mechanism for various influenza strains to compete or to circulate, to possibly provide predictors of future trends in the evolution of influenza viruses of various subtypes, and perhaps more importantly, to be of use to future annual seasonal influenza vaccine design.

Ebola outbreak in West Africa: real-time estimation and multiple-wave prediction.

Based on the reported data until 18 March 2015 and numerical fitting via a simple formula of cumulative case number, we provide real-time estimation on basic reproduction number, inflection point, peak time and final outbreak size of ongoing Ebola outbreak in West Africa. From our simulation, we conclude that the first wave has passed its inflection point and predict that a second epidemic wave may appear in the near future.

Modeling the 2014 Ebola Virus Epidemic - Agent-Based Simulations, Temporal Analysis and Future Predictions for Liberia and Sierra Leone.

We developed an agent-based model to investigate the epidemic dynamics of Ebola virus disease (EVD) in Liberia and Sierra Leone from May 27 to December 21, 2014. The dynamics of the agent-based simulator evolve on small-world transmission networks of sizes equal to the population of each country, with adjustable densities to account for the effects of public health intervention policies and individual behavioral responses to the evolving epidemic. Based on time series of the official case counts from the World Health Organization (WHO), we provide estimates for key epidemiological variables by employing the so-called Equation-Free approach. The underlying transmission networks were characterized by rather random structures in the two countries with densities decreasing by ~19% from the early (May 27-early August) to the last period (mid October-December 21). Our estimates for the values of key epidemiological variables, such as the mean time to death, recovery and the case fatality rate, are very close to the ones reported by the WHO Ebola response team during the early period of the epidemic (until September 14) that were calculated based on clinical data. Specifically, regarding the effective reproductive number Re, our analysis suggests that until mid October, Re was above 2.3 in both countries; from mid October to December 21, Re dropped well below unity in Liberia, indicating a saturation of the epidemic, while in Sierra Leone it was around 1.9, indicating an ongoing epidemic. Accordingly, a ten-week projection from December 21 estimated that the epidemic will fade out in Liberia in early March; in contrast, our results flashed a note of caution for Sierra Leone since the cumulative number of cases could reach as high as 18,000, and the number of deaths might exceed 5,000, by early March 2015. However, by processing the reported data of the very last period (December 21, 2014-January 18, 2015), we obtained more optimistic estimates indicative of a remission of the epidemic in Sierra Leone, as reflected by the derived Re (~0.82, 95% CI: 0.81-0.83).