scholarly journals Does sunlight drive seasonality of TB in Vietnam? A retrospective environmental ecological study of Tuberculosis seasonality in Vietnam from 2010-2015

2019 ◽  
Author(s):  
Ana Bonell ◽  
Lucie Contamin ◽  
Pham Quang Thai ◽  
Hoang Thi Thanh Thuy ◽  
Rogier H van Doorn ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Model ◽  
Spatial Clustering ◽  
Absolute Humidity ◽  
World Health ◽  
Infection Prevalence ◽  
Spatial And Temporal Patterns ◽  
Mixed Effect ◽  
The World ◽  
Temporal And Spatial

Abstract Background Tuberculosis (TB) is a major global health burden, with an estimated quarter of the world’s population being infected. The World Health Organization (WHO) launched the “End TB Strategy” in 2014 with emphasis placed on knowing the epidemic. Vietnam is 12th in the world ranking of high burden countries by WHO definition. TB spatial and temporal patterns have been observed globally with evidence of Vitamin D playing a role in the seasonality. We explored the presence of temporal and spatial clustering of TB in Vietnam and their determinants to aid public health measures.Methods Data were collected by the National TB program of Vietnam from 2010-2015 and linked to the following datasets: socio-demographic characteristics; climatic variables; influenza-like-illness (ILI) incidence; geospatial data. The TB dataset was aggregated by province and quarter. Descriptive time series analyses using LOESS regression were completed per province to determine seasonality and trend. Harmonic regression was used to determine the amplitude of seasonality by province. A mixed-effect linear model was used with province and year as random effects and all other variables as fixed effects.Results There were 610,676 cases of TB notified between 2010-2015 in Vietnam. Heat maps of TB incidence per quarter per province showed substantial temporal and geospatial variation. Time series analysis demonstrated seasonality throughout the country, with peaks in spring/summer and troughs in autumn/winter. Incidence was consistently higher in the south. However, relative seasonal amplitude was more pronounced in the north. Mixed-effect linear model confirmed that TB incidence was associated with time and latitude. Of the demographic, socio-economic and health related variables, population density, percentage of those under 15 years of age, and HIV infection prevalence per province were associated with TB incidence. Of the climate variables, absolute humidity, average temperature and sunlight were associated with TB incidence.Conclusion Incidence decreased and the relative seasonal amplitude of TB increased with latitude in Vietnam. Temporal and spatial hotspots were found. Seasonality of TB was associated with reduced hours of sunlight at a lag of six months.

scholarly journals Does sunlight drive seasonality of TB in Vietnam? A retrospective environmental ecological study of Tuberculosis seasonality in Vietnam from 2010-2015

2019 ◽  
Author(s):  
Ana Bonell ◽  
Lucie Contamin ◽  
Pham Quang Thai ◽  
Hoang Thi Thanh Thuy ◽  
Rogier H van Doorn ◽  
...  
Keyword(s):  
Public Health ◽  
Vitamin D ◽  
Time Series ◽  
Linear Model ◽  
World Health ◽  
Infection Prevalence ◽  
The South ◽  
Mixed Effect ◽  
Health Measures ◽  
The World

Abstract Background: Tuberculosis (TB) is a major global health burden, with an estimated quarter of the world’s population being infected. The World Health Organization (WHO) launched the “End TB Strategy” in 2014 emphasising knowing the epidemic. WHO ranks Vietnam 12 th in the world of high burden countries. TB spatial and temporal patterns have been observed globally with evidence of Vitamin D playing a role in seasonality. We explored the presence of temporal and spatial clustering of TB in Vietnam and their determinants to aid public health measures. Methods: Data were collected by the National TB program of Vietnam from 2010-2015 and linked to the following datasets: socio-demographic characteristics; climatic variables; influenza-like-illness (ILI) incidence; geospatial data. The TB dataset was aggregated by province and quarter. Descriptive time series analyses using LOESS regression were completed per province to determine seasonality and trend. Harmonic regression was used to determine the amplitude of seasonality by province. A mixed-effect linear model was used with province and year as random effects and all other variables as fixed effects. Results: There were 610,676 cases of TB notified between 2010-2015 in Vietnam. Heat maps of TB incidence per quarter per province showed substantial temporal and geospatial variation. Time series analysis demonstrated seasonality throughout the country, with peaks in spring/summer and troughs in autumn/winter. Incidence was consistently higher in the south, the three provinces with the highest incidence per 100,000 population were Tay Ninh, An Giang and Ho Chi Minh City. However, relative seasonal amplitude was more pronounced in the north. Mixed-effect linear model confirmed that TB incidence was associated with time and latitude. Of the demographic, socio-economic and health related variables, population density, percentage of those under 15 years of age, and HIV infection prevalence per province were associated with TB incidence. Of the climate variables, absolute humidity, average temperature and sunlight were associated with TB incidence. Conclusion : Preventative public health measures should be focused in the south of Viet Nam where incidence is highest. Vitamin D is unlikely to be a strong driver of seasonality but supplementation may play a role in a package of interventions.

scholarly journals Impact of campaign-style delivery of routine vaccines during Intensified Mission Indradhanush in India: a controlled interrupted time-series analysis

2020 ◽  
Author(s):  
Emma Clarke-Deelder ◽  
Christian Suharlim ◽  
Susmita Chatterjee ◽  
Logan Brenzel ◽  
Arindam Ray ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Immunization Coverage ◽  
Interrupted Time Series ◽  
Childhood Vaccination ◽  
World Health ◽  
Interrupted Time Series Analysis ◽  
Series Analysis ◽  
The World ◽  
The Impact

AbstractIntroductionThe world is not on track to achieve the goals for immunization coverage and equity described by the World Health Organization’s Global Vaccine Action Plan. In India, only 62% of children had received a full course of basic vaccines in 2016. We evaluated the Intensified Mission Indradhanush (IMI), a campaign-style intervention to increase routine immunization coverage and equity in India, implemented in 2017-2018.MethodsWe conducted a comparative interrupted time-series analysis using monthly district-level data on vaccine doses delivered, comparing districts participating and not participating in IMI. We estimated the impact of IMI on coverage and under-coverage (defined as the proportion of children who were unvaccinated) during the four-month implementation period and in subsequent months.FindingsDuring implementation, IMI increased delivery of thirteen infant vaccines by between 1.6% (95% CI: −6.4, 10.2%) and 13.8% (3.0%, 25.7%). We did not find evidence of a sustained effect during the 8 months after implementation ended. Over the 12 months from the beginning of implementation, IMI reduced under-coverage of childhood vaccination by between 3.9% (−6.9%, 13.7%) and 35.7% (−7.5%, 77.4%). The largest estimated effects were for the first doses of vaccines against diptheria-tetanus-pertussis and polio.InterpretationIMI had a substantial impact on infant immunization delivery during implementation, but this effect waned after implementation ended. Our findings suggest that campaign-style interventions can increase routine infant immunization coverage and reach formerly unreached children in the shorter term, but other approaches may be needed for sustained coverage improvements.FundingBill & Melinda Gates Foundation.

Use of the World Health Organization Access, Watch, and Reserve Classification to Follow Trends in Prescription of Antibiotic Use in Two Pediatric Intensive Care Units in Rio de Janeiro, Brazil

2020 ◽  
Vol 15 (03) ◽  
pp. 155-160
Author(s):  
André Ricardo Araujo da Silva ◽  
Cristina Vieira de Souza Oliveira ◽  
Cristiane Henriques Teixeira ◽  
Izabel Alves Leal
Keyword(s):  
Time Series ◽  
Intensive Care ◽  
Time Series Analysis ◽  
World Health Organization ◽  
Pediatric Intensive Care ◽  
Antibiotic Use ◽  
Antibiotic Consumption ◽  
World Health ◽  
The World ◽  
Health Organization

Abstract Objective The recommended percentage of antibiotic use in pediatric intensive care units (PICUs) using the World Health Organization (WHO) Access, Watch, and Reserve (AWaRE) classification is not known. Methods We have conducted an interrupted time series analysis in two PICUs in Rio de Janeiro, Brazil, over a period of 18 months. The type of antibiotics used was evaluated using the WHO AWaRE classification, and the amount of antibiotic was measured using days of therapy/1,000 patient-days (DOT/1000PD) after implementation of an antimicrobial stewardship program (ASP). The first and last semesters were compared using medians and the Mann–Whitney's test. The trends of antibiotic consumption were performed using time series analysis in three consecutive 6-month periods. Results A total of 2,205 patients were admitted, accounting for 12,490 patient-days. In PICU 1, overall antibiotic consumption (in DOT/1000PD) was 1,322 in the first 6 months of analysis and 1,264.5 in the last 6 months (p = 0.81). In PICU 2, the consumption for the same period was 1,638.5 and 1,344.5, respectively (p = 0.031). In PICU 1, the antibiotics classified in the AWaRE groups were used 33.2, 57.9, and 8.4% of the time, respectively. The remaining 0.5% of antibiotics used were not classified in any of these groups. In PICU 2, the AWaRE groups corresponded to 30.2, 60.5, and 9.3% of all antibiotics used, respectively. There was no use of unclassified antibiotics in this unit. The use of all three groups of WHO AWaRE antibiotics was similar in the first and the last semesters, with the exception of Reserve group in PICU 2 (183.5 × 92, p = 0.031). Conclusion A significant reduction of overall antibiotic use and also in the Reserve group was achieved in one of the PICU units studied. The antibiotics classified in the Watch group were the most used in both units, representing ∼60% of all the antibiotics consumed.

scholarly journals Time series analysis of tuberculosis registration rates from 2007 to 2019 and prediction: could tuberculosis control milestones be achieved in 2020 and 2025 in Henan Province, China?

2020 ◽  
Author(s):  
Yanqiu Zhang ◽  
Weibin Li ◽  
Jianguo Jiang ◽  
Guolong Zhang ◽  
Yan Zhuang ◽  
...  
Keyword(s):  
Time Series ◽  
Confidence Interval ◽  
Early Response ◽  
Information Management System ◽  
Early Summer ◽  
Henan Province ◽  
World Health ◽  
The World ◽  
Response Measures ◽  
Health Organization

Abstract Background: The World Health Organization (WHO) End TB Strategy meant that compared with 2015 baseline, the reduction in pulmonary tuberculosis(PTB) incidence rate should be 20% and 50% in 2020 and 2025, respectively. The incidence number of PTB in China accounted for 9% of the global total in 2018, which ranked the second high in the world. From 2007 to 2019, 854,672active PTB cases were registered and treated in Henan Province, China. We need to assess whether the WHO milestones could be achieved in Henan Province. Methods: The active PTB numbers in Henan Province from 2007 to2019, registered in Chinese Tuberculosis Information Management System (CTIMS) were analyzed to predict the active PTB registration rates in 2020 and 2025, which is conductive to early response measures to ensure the achievement of the WHO milestones. The time series model was created by monthly active PTB registration rates from 2007 to 2016, and the optimal model was verified by data from 2017 to 2019. Monthly and annual active PTB registration rates and 95% confidence interval (CI) from 2020 to 2025 were predicted. Results: High active PTB registration rates in March, April, May and June showed the seasonal variations. The exponential smoothing winter’s multiplication model was selected as the best-fitting model. The predicted values were approximately consistent with the observed ones from 2017 to 2019. The annual active PTB registration rates were predicted as 49.2 (95% CI: 36.0-62.5) and 34.3 (95% CI: 17.7-50.8) per 100 ,000 population in 2020 and 2025 , respectively. Compared with the active PTB registration rate in 2015, the reduction will reach 23.7% (95% CI: 3.1%-44.2%) and 46.9% (95% CI: 21.3%-72.5%) in 2020 and 2025, respectively. Conclusions: The high active PTB registration rates in spring and early summer indicates that high risk of tuberculosis infection in late autumn and winter in Henan Province. Without regard to the confidence interval, the first milestone of WHO End TB Strategy in 2020 will be achieved. However, the second milestone in 2025 will not be easily achieved unless there are early response measures in Henan Province, China. Trial registration: Not applicable

scholarly journals Contradicción y paradoja en el reporte de felicidad mundial, una mirada objetiva

2014 ◽  
Vol 2 (1) ◽  
Author(s):  
Raúl Carpio
Keyword(s):  
United Nations ◽  
World Health Organization ◽  
Linear Model ◽  
Suicide Rate ◽  
World Health ◽  
Simple Correlation ◽  
The World ◽  
The United Nations ◽  
Health Organization ◽  
Modelo Lineal

El presente trabajo, presenta un análisis que propone un modelo lineal inverso, mediante correlación simple. Se utilizan las variables, nivel de felicidad de un país, emitido en el Reporte de Felicidad Mundial, de la Organización de las Naciones Unidas, y la tasa de suicidios del país emitida por la Organización Mundial de la Salud. Este estudio, es un intento de probar la utilidad del mencionado ranking de felicidad y si se lo puede usar como un referente de la situación emocional de las naciones.AbstractThis work shows an analysis who proposes an inverse linear model by simple correlation. It uses the variables: country ranking of happiness; publish in the World Happiness Report of the United Nations, and the country suicide rate, published by the World Health Organization. This study tries to probe the usefulness of the happiness ranking, and if it´s a good reference about the emotional situation of the nations.

scholarly journals Effects of Weather on Coronavirus Pandemic

2020 ◽  
Vol 17 (15) ◽  
pp. 5399 ◽  
Author(s):  
Qasim Bukhari ◽  
Joseph M. Massaro ◽  
Ralph B. D’Agostino ◽  
Sheraz Khan
Keyword(s):  
Absolute Humidity ◽  
Health Interventions ◽  
World Health ◽  
The Novel ◽  
The World ◽  
Novel Coronavirus ◽  
Health Organization ◽  
Effective Public Health ◽  
Local Weather ◽  
Peak Summer

The novel coronavirus (SARS-CoV-2) has spread globally and has been declared a pandemic by the World Health Organization. While influenza virus shows seasonality, it is unknown if COVID-19 has any weather-related affect. In this work, we analyze the patterns in local weather of all the regions affected by COVID-19 globally. Our results indicate that approximately 85% of the COVID-19 reported cases until 1 May 2020, making approximately 3 million reported cases (out of approximately 29 million tests performed) have occurred in regions with temperature between 3 and 17 °C and absolute humidity between 1 and 9 g/m3. Similarly, hot and humid regions outside these ranges have only reported around 15% or approximately 0.5 million cases (out of approximately 7 million tests performed). This suggests that weather might be playing a role in COVID-19 spread across the world. However, this role could be limited in US and European cities (above 45 N), as mean temperature and absolute humidity levels do not reach these ranges even during the peak summer months. For hot and humid countries, most of them have already been experiencing temperatures >35 °C and absolute humidity >9 g/m3 since the beginning of March, and therefore the effect of weather, however little it is, has already been accounted for in the COVID-19 spread in those regions, and they must take strict social distancing measures to stop the further spread of COVID-19. Our analysis showed that the effect of weather may have only resulted in comparatively slower spread of COVID-19, but not halted it. We found that cases in warm and humid countries have consistently increased, accounting for approximately 500,000 cases in regions with absolute humidity >9 g/m3, therefore effective public health interventions must be implemented to stop the spread of COVID-19. This also means that ‘summer’ would not alone stop the spread of COVID-19 in any part of the world.

Jaya Spider Monkey Optimization-driven Deep Convolutional LSTM for the prediction of COVID’19

2020 ◽  
Vol 16 (4) ◽  
Author(s):  
Satish Chander ◽  
Vijaya Padmanabha ◽  
Joseph Mani
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Time Series Data ◽  
Moving Average ◽  
Spider Monkey ◽  
World Health ◽  
Series Data ◽  
Mean Square ◽  
The World ◽  
Spider Monkey Optimization

AbstractCOVID’19 is an emerging disease and the precise epidemiological profile does not exist in the world. Hence, the COVID’19 outbreak is treated as a Public Health Emergency of the International Concern by the World Health Organization (WHO). Hence, an effective and optimal prediction of COVID’19 mechanism, named Jaya Spider Monkey Optimization-based Deep Convolutional long short-term classifier (JayaSMO-based Deep ConvLSTM) is proposed in this research to predict the rate of confirmed, death, and recovered cases from the time series data. The proposed COVID’19 prediction method uses the COVID’19 data, which is the trending domain of research at the current era of fighting the COVID’19 attacks thereby, to reduce the death toll. However, the proposed JayaSMO algorithm is designed by integrating the Spider Monkey Optimization (SMO) with the Jaya algorithm, respectively. The Deep ConvLSTM classifier facilitates to predict the COVID’19 from the time series data based on the fitness function. Besides, the technical indicators, such as Relative Strength Index (RSI), Rate of Change (ROCR), Exponential Moving Average (EMA), Williams %R, Double Exponential Moving Average (DEMA), and Stochastic %K, are extracted effectively for further processing. Thus, the resulted output of the proposed JayaSMO-based Deep ConvLSTM is employed for COVID’19 prediction. Moreover, the developed model obtained the better performance using the metrics, like Mean Square Error (MSE), and Root Mean Square Error (RMSE) by considering confirmed, death, and the recovered cases of COVID’19 for China and Oman. Thus, the proposed JayaSMO-based Deep ConvLSTM showed improved results with a minimal MSE of 1.791, and the minimal RMSE of 1.338 based on confirmed cases in Oman. In addition, the developed model achieved the death cases with the values of 1.609, and 1.268 for MSE and RMSE, whereas the MSE and the RMSE value of 1.945, and 1.394 is achieved by the developed model using recovered cases in China.

scholarly journals Evaluation of the epidemiological behavior of mortality due to COVID-19 in Brazil: A time series study

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256169
Author(s):  
Ketyllem Tayanne da Silva Costa ◽  
Thiffany Nayara Bento de Morais ◽  
Dayane Caroliny Pereira Justino ◽  
Fábia Barbosa de Andrade
Keyword(s):  
Time Series ◽  
Secondary Data ◽  
Health Surveillance ◽  
World Health ◽  
Microsoft Excel ◽  
Time Series Study ◽  
The World ◽  
Independent Variable ◽  
Health Organization ◽  
Series Study

The World Health Organization declared, at the end of 2019, a pandemic caused by SARS-CoV-2, a virus that causes Coronavirus Disease—COVID-19. Currently, Brazil has become the epicenter of the disease, registering approximately 345 thousand deaths. Thus, the study has scientific relevance in health surveillance as it identifies, quantifies and monitors the main behavioral patterns of the mortality rate due to COVID-19, in Brazil and in their respective regions. In this context, the study aims to assess the epidemiological behavior of mortality due to COVID-19 in Brazil: a time series study, referring to the year 2020. This is an ecological time series study, constructed using secondary data. The research was carried out in Brazil, having COVID-19 deaths as the dependent variable that occurred between the 12th and 53rd Epidemiological Week of 2020. The independent variable will be the epidemiological weeks. The data on deaths by COVID-19 were extracted in February 2021, on the Civil Registry Transparency Portal. The cleaning of the database and the information were treated in the Microsoft Excel® Software and, for statistical analysis, the JoinPoint software, version 4.7.0.0 was used. It was observed that Brazil presents an upward curve between the 12th and 19th SE, when it reaches saturation at the peak of mortality, which remains until the 35th SE and, subsequently, a downward curve was identified until the 47th SE, period in the which curve turns back up.

scholarly journals Effects of government policies and the Nowruz holidays on confirmed COVID-19 cases in Iran: An intervention time series analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0256516
Author(s):  
Ali Hadianfar ◽  
Razieh Yousefi ◽  
Milad Delavary ◽  
Vahid Fakoor ◽  
Mohammad Taghi Shakeri ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Government Policies ◽  
World Health ◽  
Social Distancing ◽  
Social Interventions ◽  
Series Analysis ◽  
Daily Data ◽  
The World ◽  
Health Organization

Background Public health policies with varying degrees of restriction have been imposed around the world to prevent the spread of coronavirus disease 2019 (COVID-19). In this study, we aimed to evaluate the effects of the implementation of government policies and the Nowruz holidays on the containment of the COVID-19 pandemic in Iran, using an intervention time series analysis. Methods Daily data on COVID-19 cases registered between February 19 and May 2, 2020 were collected from the World Health Organization (WHO)’s website. Using an intervention time series modeling, the effect of two government policies on the number of confirmed cases were evaluated, namely the closing of schools and universities, and the implementation of social distancing measures. Furthermore, the effect of the Nowruz holidays as a non-intervention factor for the spread of COVID-19 was also analyzed. Results The results showed that, after the implementation of the first intervention, i.e., the closing of universities and schools, no statistically significant change was found in the number of new confirmed cases. The Nowruz holidays was followed by a significant increase in new cases (1,872.20; 95% CI, 1,257.60 to 2,476.79; p<0.001)), while the implementation of social distancing measures was followed by a significant decrease in such cases (2,182.80; 95% CI, 1,556.56 to 2,809.04; p<0.001). Conclusion The Nowruz holidays and the implementation of social distancing measures in Iran were related to a significant increase and decrease in COVID-19 cases, respectively. These results highlight the necessity of measuring the effect of health and social interventions for their future implementations.

scholarly journals Disdrometer Network Observations of Finescale Spatial–Temporal Clustering in Rain

2015 ◽  
Vol 72 (4) ◽  
pp. 1648-1666 ◽  
Author(s):  
A. R. Jameson ◽  
M. L. Larsen ◽  
A. B. Kostinski
Keyword(s):  
Time Series ◽  
South Carolina ◽  
Drop Size ◽  
Spatial Clustering ◽  
Temporal Clustering ◽  
Temporal Structures ◽  
Average Total Number ◽  
Temporal And Spatial ◽  
Spatial Size ◽  
Advection Velocity

Abstract The spatial clustering of drops is a defining characteristic of rain on all scales from centimeters to kilometers. It is the physical basis for much of the observed variability in rain. The authors report here on the temporal–spatial 1-min counts using a network of 21 optical disdrometers over a small area near Charleston, South Carolina. These observations reveal significant differences between spatial and temporal structures (i.e., clustering) for different sizes of drops, which suggest that temporal observations of clustering cannot be used to infer spatial clustering simply using by an advection velocity as has been done in past studies. It is also shown that both spatial and temporal clustering play a role in rain variability depending upon the drop size. The more convective rain is dominated by spatial clustering while the opposite holds for the more stratiform rain. Like previous time series measurements by a single disdrometer but in contradiction with widely accepted drop size distribution power-law relations, it is also shown that there is a linear relation between 1-min averages of the rainfall rate R over the network and the average total number of drops Nt. However, the network (area) R–Nt relation differs from those derived strictly from time series observations by individual disdrometers. These differences imply that the temporal and spatial size distributions and their variabilities are not equivalent.

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