scholarly journals Study of the Dependence of Effective Reproduction Number of COVID-19 on the Temperature and Humidity: A Case Study with the Indian States

2020 ◽  
Author(s):  
Manoj Mandal ◽  
Subhradeep Patra ◽  
Sabyasachi Pal ◽  
Suman Acharya ◽  
Mangal Hazra
Keyword(s):  
Relative Humidity ◽  
Reproduction Number ◽  
World Health ◽  
Mitigation Measures ◽  
Negative Slope ◽  
Maximum Temperature ◽  
Linear Regression Method ◽  
Indian States ◽  
Temperature And Humidity ◽  
The Impact

Corona Virus Disease 2019 (COVID-19) started in Wuhan province of China in November 2019 and within a short time, it was declared as a worldwide pandemic by World Health Organisation due to very fast worldwide spread of the virus. In the absence of any vaccine, various mitigation measures were used. In the past, the effect of temperature and humidity on the spread of the virus was studied for a very early phase of the data with mixed results. We are studying the impact of COVID-19 on the maximum temperature and relative humidity of a place using Indian states as test cases for SIR, SIRD, and SEIR models. We used a linear regression method to look for any dependency between effective reproduction number with maximum temperature and relative humidity. Most of the states show a correlation with the negative slope between the effective reproduction number with the maximum temperature and the relative humidity. It indicates that the effective reproduction number goes down as maximum temperature or relative humidity rise. But, the regression coefficient R2 is low for these correlations which means that the correlation is not strong.

Characterizing the effectiveness of COVID-19 mitigation measures: A data-centric approach (Preprint)

2020 ◽  
Author(s):  
Lukman Olagoke ◽  
Ahmet E. Topcu
Keyword(s):  
Control Measure ◽  
Control Measures ◽  
World Health ◽  
Mitigation Measures ◽  
Effective Control ◽  
The World ◽  
Decision Making Processes ◽  
And Performance ◽  
Health Organization ◽  
The Impact

BACKGROUND COVID-19 represents a serious threat to both national health and economic systems. To curb this pandemic, the World Health Organization (WHO) issued a series of COVID-19 public safety guidelines. Different countries around the world initiated different measures in line with the WHO guidelines to mitigate and investigate the spread of COVID-19 in their territories. OBJECTIVE The aim of this paper is to quantitatively evaluate the effectiveness of these control measures using a data-centric approach. METHODS We begin with a simple text analysis of coronavirus-related articles and show that reports on similar outbreaks in the past strongly proposed similar control measures. This reaffirms the fact that these control measures are in order. Subsequently, we propose a simple performance statistic that quantifies general performance and performance under the different measures that were initiated. A density based clustering of based on performance statistic was carried out to group countries based on performance. RESULTS The performance statistic helps evaluate quantitatively the impact of COVID-19 control measures. Countries tend show variability in performance under different control measures. The performance statistic has negative correlation with cases of death which is a useful characteristics for COVID-19 control measure performance analysis. A web-based time-line visualization that enables comparison of performances and cases across continents and subregions is presented. CONCLUSIONS The performance metric is relevant for the analysis of the impact of COVID-19 control measures. This can help caregivers and policymakers identify effective control measures and reduce cases of death due to COVID-19. The interactive web visualizer provides easily digested and quick feedback to augment decision-making processes in the COVID-19 response measures evaluation. CLINICALTRIAL Not Applicable

The impact of botfly parasitism on the health of the gracile mouse opossum (Gracilinanus agilis)

Parasitology ◽  
2019 ◽  
Vol 146 (8) ◽  
pp. 1013-1021
Author(s):  
Priscilla Lóra Zangrandi ◽  
André Faria Mendonça ◽  
Ariovaldo Pereira Cruz-Neto ◽  
Rudy Boonstra ◽  
Emerson M. Vieira
Keyword(s):  
Relative Humidity ◽  
Body Condition ◽  
Animal Health ◽  
Haemoglobin Concentration ◽  
Blood Parameters ◽  
Small Population ◽  
Maximum Temperature ◽  
Food Supplementation ◽  
Local Extinctions ◽  
The Impact

AbstractFragmented habitats generally harbour small populations that are potentially more prone to local extinctions caused by biotic factors such as parasites. We evaluated the effects of botflies (Cuterebra apicalis) on naturally fragmented populations of the gracile mouse opossum (Gracilinanus agilis). We examined how sex, food supplementation experiment, season and daily climatic variables affected body condition and haemoglobin concentration in animals that were parasitized or not by botflies. Although parasitism did not affect body condition, haemoglobin concentrations were lower in parasitized animals. Among the non-parasitized individuals, haemoglobin concentration increased with the increase of maximum temperature and the decrease of relative humidity, a climatic pattern found at the peak of the dry season. However, among parasitized animals, the opposite relationship between haemoglobin concentration and relative humidity occurred, as a consequence of parasite-induced anaemia interacting with dehydration as an additional stressor. We conclude that it is critical to assess how climate affects animal health (through blood parameters) to understand the population consequences of parasitism on the survival of individuals and hence of small population viability.

scholarly journals The impact of climate change on the resin productivity of dammar tree (Agathis alba) in Inamosol subdistrict, West Seram district, Maluku-Indonesia

2021 ◽  
Vol 883 (1) ◽  
pp. 012079
Author(s):  
J M Matinahoru
Keyword(s):  
Climate Change ◽  
Relative Humidity ◽  
Sampling Technique ◽  
Climate Data ◽  
Purposive Sampling ◽  
Impact Of Climate Change ◽  
Average Temperature ◽  
Change Factors ◽  
Temperature And Humidity ◽  
The Impact

Abstract This research was aimed to determine the impact of climate change on the resin productivity of dammar tree. This research will be useful as data and information for farmers and government to maintain the resin of dammar tree to be optimal and sustainable in production. This research was conducted in Inamosol Sub-district, West Seram District, Maluku Indonesia, during September-October 2020. Village and farmer samples were determined by purposive sampling technique. The selected villages were Honitetu, Hukuanakota and Rambatu. Furthermore, from each village, It was ten farmers to select for interviews and filling the questionnaire. The results showed that the average resin production of farmers in 2019 was 904.2 kg/farmer, while in 2020 was 523.7 kg/farmer. This means that it occurred a decline in resin production in 2020 about 42.08 % for each farmer—the leading cause of the decreased production as climate change factors, namely rainfall, temperature and humidity. Based on climate data of West Seram District in 2019 indicated that rainfall has occurred during six months with an average temperature of 27 °C and relative humidity of 82 %. Meanwhile, in 2020 the rainfall occurs for nine months with an average temperature of 26.5 °C, and relative humidity of 85 %.

scholarly journals Quantifying the impact of heat on human physical work capacity; part II: the observed interaction of air velocity with temperature, humidity, sweat rate, and clothing is not captured by most heat stress indices

2021 ◽  
Author(s):  
Josh Foster ◽  
James W. Smallcombe ◽  
Simon Hodder ◽  
Ollie Jay ◽  
Andreas D. Flouris ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Empirical Study ◽  
Air Temperature ◽  
Physical Work ◽  
Climate Index ◽  
Physical Work Capacity ◽  
Air Movement ◽  
Wide Range ◽  
Temperature And Humidity ◽  
The Impact

Abstract Increasing air movement can alleviate or exacerbate occupational heat strain, but the impact is not well defined across a wide range of hot environments, with different clothing levels. Therefore, we combined a large empirical study with a physical model of human heat transfer to determine the climates where increased air movement (with electric fans) provides effective body cooling. The model allowed us to generate practical advice using a high-resolution matrix of temperature and humidity. The empirical study involved a total of 300 1-h work trials in a variety of environments (35, 40, 45, and 50 °C, with 20 up to 80% relative humidity) with and without simulated wind (3.5 vs 0.2 m∙s−1), and wearing either minimal clothing or a full body work coverall. Our data provides compelling evidence that the impact of fans is strongly determined by air temperature and humidity. When air temperature is ≥ 35 °C, fans are ineffective and potentially harmful when relative humidity is below 50%. Our simulated data also show the climates where high wind/fans are beneficial or harmful, considering heat acclimation, age, and wind speed. Using unified weather indices, the impact of air movement is well captured by the universal thermal climate index, but not by wet-bulb globe temperature and aspirated wet-bulb temperature. Overall, the data from this study can inform new guidance for major public and occupational health agencies, potentially maintaining health and productivity in a warming climate.

scholarly journals Long term monthly and inter-seasonal weather variability analysis for the lower Shivalik foothills of Punjab

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 173-180
Author(s):  
NAVNEET KAUR ◽  
M.J. SINGH ◽  
SUKHJEET KAUR
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Linear Regression Method ◽  
Post Monsoon ◽  
Sunshine Hours ◽  
Annual Minimum Temperature ◽  
Weather Parameters

This paper aims to study the long-term trends in different weather parameters, i.e., temperature, rainfall, rainy days, sunshine hours, evaporation, relative humidity and temperature over Lower Shivalik foothills of Punjab. The daily weather data of about 35 years from agrometeorological observatory of Regional Research Station Ballowal Saunkhri representing Lower Shivalik foothills had been used for trend analysis for kharif (May - October), rabi (November - April), winter (January - February), pre-monsoon (March - May), monsoon (June - September) and post monsoon (October - December) season. The linear regression method has been used to estimate the magnitude of change per year and its coefficient of determination, whose statistical significance was checked by the F test. The annual maximum temperature, morning and evening relative humidity has increased whereas rainfall, evaporation sunshine hours and wind speed has decreased significantly at this region. No significant change in annual minimum temperature and diurnal range has been observed. Monthly maximum temperature revealed significant increase except January, June and December, whereas, monthly minimum temperature increased significantly for February, March and October and decreased for June. Among different seasons, maximum temperature increased significantly for all seasons except winter season, whereas, minimum temperature increased significantly for kharif and post monsoon season only. The evaporation, sunshine hours and wind speed have also decreased and relative humidity decreased significantly at this region. Significant reduction in kharif, monsoon and post monsoon rainfall has been observed at Lower Shivalik foothills. As the region lacks assured irrigation facilities so decreasing rainfall and change in the other weather parameters will have profound effects on the agriculture in this region so there is need to develop climate resilient agricultural technologies.

scholarly journals IS CLIMATE INDICATOR AFFECTS TRANSMISSION OF COVID-19 PANDEMIC: CLIMATIC IMPACTS ON COVID-19 TRANSMISSION

2021 ◽  
Vol 37 (01) ◽  
pp. 35-42
Author(s):  
Muhammad Asif Khan ◽  
Muhammad Waseem Khan ◽  
Asima Siddique
Keyword(s):  
Positive Impact ◽  
Virus Transmission ◽  
Secondary Data ◽  
Vital Role ◽  
Economic Consequences ◽  
World Health ◽  
Maximum Temperature ◽  
Wind Flow ◽  
Temperature Minimum ◽  
The Impact

The climate variations have lot of the financial, medical and economic consequences. Studies showed that climate plays the vital role in virus transmission. This study analyzed the impact of climate indicators on COVID-19 concerning Pakistan. The secondary data is used for analysis as obtained from world health organization, ministry of health Pakistan and Pakistan meteorological department. The results show that all the research variables like temperature maximum, temperature minimum, humidity, and wind flow are positively and significantly correlated to COVID-19. Findings show that temperature maximum, temperature minimum, and wind flow have the positive and significant association with COVID-19, while the humidity has a positive impact on COVID-19 transmission. The study show that minimum temperature is favorable for the virus transmission. Thus, the study provides significant results in reaching the decision and concluding the study.

scholarly journals Modelling COVID-19 cases in Nigeria: Forecasts, uncertainties, projections and the link with weather

2020 ◽  
Author(s):  
Adeyeri O.E. ◽  
Oyekan K.S.A. ◽  
Ige S.O. ◽  
Akinbobola A. ◽  
Okogbue E.C.
Keyword(s):  
Correlation Coefficient ◽  
Exponential Growth ◽  
Reproduction Number ◽  
Correlation Coefficients ◽  
World Health ◽  
Estimation Methods ◽  
Maximum Temperature ◽  
Religious Activities ◽  
Epidemic Curve ◽  
First Case

Abstract The World Health Organization (WHO) declared COVID-19 a global pandemic on 11 March 2020 due to its global spread. In Nigeria, the first case was documented on 27 February 2020. Since then, it has spread to most parts of the country. This study models, forecasts and projects COVID-19 incidence, cumulative incidence and death cases in Nigeria using six estimation methods i.e. the attack rate, maximum likelihood, exponential growth, Markov chain monte Carlo (MCMC), time-dependent and the sequential Bayesian approaches. A sensitivity analysis with respect to the mean generation time is used to quantify the associated reproduction number uncertainties. The relationship between the COVID-19 incidence and five meteorological variables are further assessed. The result shows that the highest incidences are recorded in days with either religious activities or market days while the weekday trend decreases towards the weekend. It is also established that COVID-19 incidence significantly increases with increasing sea level pressure (0.7 correlation coefficient) and significantly decreases with increasing maximum temperature (-0.3 correlation coefficient). Also, selecting an optimal period for reproduction number estimates reduces the variability between estimates. As an example, in the EG approach, the epidemic curve that optimally fits the exponential growth is between 1- and 53-time units with reproduction number estimate of 1.60 [1.58; 1.62] at 95% confidence interval. However, this optimal reproduction number estimate is different from the default reproduction number estimate. Using the MCMC approach, the correlation coefficients between the observed and forecasted incidence, cumulative death and cumulative confirmed cases are 0.66, 0.92 and 0.90 respectively. The projections till December shows values approaching 1,000,000, 120,000 and 3,000,000 respectively. Therefore, timely intervention and effective preventive measures are immediately needed to mitigate a full-scale epidemic in the country.

scholarly journals Are we there yet? An adaptive SIR model for continuous estimation of COVID-19 infection rate and reproduction number in the United States (Preprint)

2020 ◽  
Author(s):  
Mark Shapiro ◽  
Fazle Karim ◽  
Guido Muscioni ◽  
Abel Saju Augustine
Keyword(s):  
Infection Rate ◽  
Reproduction Number ◽  
Infected Individual ◽  
Local Population ◽  
The United States ◽  
Mitigation Measures ◽  
Simple Method ◽  
Wide Range ◽  
Continuous Estimation ◽  
The Impact

BACKGROUND The dynamics of the COVID-19 epidemic vary due to local population density and policy measures. When making decisions, policy makers consider an estimate of the effective reproduction number R_t which is the expected number of secondary infections by a single infected individual. OBJECTIVE We propose a simple method for estimating the time-varying infection rate and reproduction number R_t . METHODS We use a sliding window approach applied to a Susceptible-Infectious-Removed model. The infection rate is estimated using the reported cases for a seven-day window to obtain continuous estimation of R_t. The proposed adaptive SIR (aSIR) model was applied to data at the state and county levels. RESULTS The aSIR model showed an excellent fit for the number of reported COVID-19 positive cases, a one-day forecast MAPE was less than 2.6% across all states. However, a seven-day forecast MAPE reached 16.2% and strongly overestimated the number of cases when the reproduction number was high and changing fast. The maximal R_t showed a wide range of 2.0 to 4.5 across all states, with the highest values for New York (4.4) and Michigan (4.5). We demonstrate that the aSIR model can quickly adapt to an increase in the number of tests and associated increase in the reported cases of infections. Our results also suggest that intensive testing may be one of the effective methods of reducing R_t. CONCLUSIONS The aSIR model provides a simple and accurate computational tool to obtain continuous estimation of the reproduction number and evaluate the impact of mitigation measures.

scholarly journals A modelling study highlights the power of detecting and isolating asymptomatic or very mildly affected individuals for COVID-19 epidemic management

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Lía Mayorga ◽  
Clara García Samartino ◽  
Gabriel Flores ◽  
Sofía Masuelli ◽  
María V. Sánchez ◽  
...  
Keyword(s):  
Compartmental Model ◽  
Reproduction Number ◽  
Mitigation Measures ◽  
Asymptomatic Group ◽  
Healthcare Burden ◽  
Fundamental Part ◽  
Management Policies ◽  
Asymptomatic Individuals ◽  
The Impact ◽  
The Ideal

Abstract Background Mathematical modelling of infectious diseases is a powerful tool for the design of management policies and a fundamental part of the arsenal currently deployed to deal with the COVID-19 pandemic. Methods We present a compartmental model for the disease where symptomatic and asymptomatic individuals move separately. We introduced healthcare burden parameters allowing to infer possible containment and suppression strategies. In addition, the model was scaled up to describe different interconnected areas, giving the possibility to trigger regionalized measures. It was specially adjusted to Mendoza-Argentina’s parameters, but is easily adaptable for elsewhere. Results Overall, the simulations we carried out were notably more effective when mitigation measures were not relaxed in between the suppressive actions. Since asymptomatics or very mildly affected patients are the vast majority, we studied the impact of detecting and isolating them. The removal of asymptomatics from the infectious pool remarkably lowered the effective reproduction number, healthcare burden and overall fatality. Furthermore, different suppression triggers regarding ICU occupancy were attempted. The best scenario was found to be the combination of ICU occupancy triggers (on: 50%, off: 30%) with the detection and isolation of asymptomatic individuals. In the ideal assumption that 45% of the asymptomatics could be detected and isolated, there would be no need for complete lockdown, and Mendoza’s healthcare system would not collapse. Conclusions Our model and its analysis inform that the detection and isolation of all infected individuals, without leaving aside the asymptomatic group is the key to surpass this pandemic.

scholarly journals Effect of Lockdown,Temperature,and Humidity on the Pattern of COVID-19 in Erbil Province,Kurdistan Region, Iraq

2021 ◽  
Vol 20 (2) ◽  
pp. 56-67
Author(s):  
Rundk Hwaiz ◽  
◽  
Katan Ali ◽  
Namir Al-Tawil
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Death Rate ◽  
Weather Conditions ◽  
World Health ◽  
The Novel ◽  
Number Of Patients ◽  
Temperature And Humidity ◽  
Novel Coronavirus ◽  
Health Organization

Background: COVID-19 was first reported in Erbil province in Iraq on March 19, 2020. The effect of lockdown on reducing the spread of the novel coronavirus and the effect of weather conditions (air temperature and humidity) on the daily reported number of cases and death rate of COVID-19 were investigated during April to July, 2020. Objective: To investigate the effect of lock down on reducing the spread of the novel coronavirus pandemic and the effect of weather conditions (air temperature and humidity) on the daily reported number of cases and death rate of COVID-19. Patients and Methods: The data collected during three different periods, the first (total lockdown), followed by the second period of lockdown relaxation, which was followed by the third period (interrupted relaxation of lockdown) that reported hundreds of new cases daily. The real-time PCR .assay was performed on suspected COVID-19 patients according to the protocol established by the World Health Organization. Results: Temperature and relative humidity were recorded in Erbil city in Iraq. Patients’ age ranged (2-70) years old. Out of (1469) patients confirmed positive with COVID-19, 57.7% of them were males, 31.3% were females, and the rest (11%) were children. The mean number of patients per day was 32.77 during the period of interrupted relaxation lockdown which was significantly higher than in the total-lock down period (3.88 patient), and the relaxation lockdown period (1.93 patient). The mortality rate per day was 0.77 during the period of interrupted relaxation lockdown was significantly higher than the rates (0.0%) of the other periods. Moreover, increasing the temperature increased the number of confirmed cases in July while, low relative humidity significantly increased the rate of reported cases. Conclusion: The increase in the number of reported cases of COVID-19, might be related to the interruption of lockdown. Moreover, the daily reported cases and mortality rates increased by increasing the temperature from April to June.

Sign in / Sign up

Export Citation Format

Share Document