scholarly journals Trend Analysis and Forecasting of COVID-19 outbreak in India

2020 ◽  
Author(s):  
Rajan Gupta ◽  
Saibal K Pal
Keyword(s):  
Arima Model ◽  
Regional Analysis ◽  
Control Measures ◽  
Government Officials ◽  
Case Scenario ◽  
Indian States ◽  
Worst Case ◽  
Medical Practitioners ◽  
Infected Case ◽  
First Case

AbstractCOVID-19 is spreading really fast around the world. The current study describes the situation of the outbreak of this disease in India and predicts the number of cases expected to rise in India. The study also discusses the regional analysis of Indian states and presents the preparedness level of India in combating this outbreak. The study uses exploratory data analysis to report the current situation and uses time-series forecasting methods to predict the future trends. The data has been considered from the repository of John Hopkins University and covers up the time period from 30th January 2020 when the first case occurred in India till the end of 24th March 2020 when the Prime Minister of India declared a complete lockdown in the country for 21 days starting 25th March 2020. The major findings show that number of infected cases in India is rising quickly with the average infected cases per day rising from 10 to 73 from the first case to the 300th case. The current mortality rate for India stands around 1.9. Kerala and Maharashtra are the top two infected states in India with more than 100 infected cases reported in each state, respectively. A total of 25 states have reported at least one infected case, however only 8 of them have reported deaths due to COVID-19. The ARIMA model prediction shows that the infected cases in India may reach up to 700 thousands in next 30 days in worst case scenario while most optimistic scenario may restrict the numbers up to 1000-1200. Also, the average forecast by ARIMA model in next 30 days is around 7000 patients from the current numbers of 536. Based on the forecasting model by Holt’s linear trends, an expected 3 million people may get infected if control measures are not taken in the near future. This study will be useful for the key stakeholders like Government Officials and Medical Practitioners in assessing the trends for India and preparing a combat plan with stringent measures. Also, this study will be helpful for data scientists, statisticians, mathematicians and analytics professionals in predicting outbreak numbers with better accuracy.

scholarly journals Trend Analysis and Forecasting of COVID-19 outbreak in India

2020 ◽  
Author(s):  
Rajan Gupta ◽  
Saibal K Pal
Keyword(s):  
Arima Model ◽  
Regional Analysis ◽  
Control Measures ◽  
Government Officials ◽  
Case Scenario ◽  
Indian States ◽  
Worst Case ◽  
Medical Practitioners ◽  
Infected Case ◽  
First Case

COVID-19 is spreading really fast around the world. The current study describes the situation of the outbreak of this disease in India and predicts the number of cases expected to rise in India. The study also discusses the regional analysis of Indian states and presents the preparedness level of India in combating this outbreak. The study uses exploratory data analysis to report the current situation and uses time-series forecasting methods to predict the future trends. The data has been considered from the repository of John Hopkins University and covers up the time period from 30th January 2020 when the first case occurred in India till the end of 24th March 2020 when the Prime Minister of India declared a complete lockdown in the country for 21 days starting 25th March 2020. The major findings show that number of infected cases in India is rising quickly with the average infected cases per day rising from 10 to 73 from the first case to the 300th case. The current mortality rate for India stands around 1.9. Kerala and Maharashtra are the top two infected states in India with more than 100 infected cases reported in each state, respectively. A total of 25 states have reported at least one infected case, however only 8 of them have reported deaths due to COVID-19. The ARIMA model prediction shows that the infected cases in India may reach up to 700 thousands in next 30 days in worst case scenario while most optimistic scenario may restrict the numbers up to 1000-1200. Also, the average forecast by ARIMA model in next 30 days is around 7000 patients from the current numbers of 536. Based on the forecasting model by Holt’s linear trends, an expected 3 million people may get infected if control measures are not taken in the near future. This study will be useful for the key stakeholders like Government Officials and Medical Practitioners in assessing the trends for India and preparing a combat plan with stringent measures. Also, this study will be helpful for data scientists, statisticians, mathematicians and analytics professionals in predicting outbreak numbers with better accuracy.

scholarly journals Imported dengue in Spain: a nationwide analysis with predictive time series analyses

2019 ◽  
Vol 26 (8) ◽  
Author(s):  
Lidia Redondo-Bravo ◽  
Claudia Ruiz-Huerta ◽  
Diana Gomez-Barroso ◽  
María José Sierra-Moros ◽  
Agustín Benito ◽  
...  
Keyword(s):  
Time Series ◽  
Control Measures ◽  
Severe Dengue ◽  
Vector Surveillance ◽  
Case Scenario ◽  
Worst Case ◽  
Temporal Behaviour ◽  
The Impact ◽  
Near Future ◽  
Local Transmission

Abstract Background Of febrile illnesses in Europe, dengue is second only to malaria as a cause of travellers being hospitalized. Local transmission has been reported in several European countries, including Spain. This study assesses the evolution of dengue-related admissions in Spain in terms of time, geographical distribution and individuals’ common characteristics; it also creates a predictive model to evaluate the risk of local transmission. Methods This is a retrospective study using the Hospital Discharge Records Database from 1997 to 2016. We calculated hospitalization rates and described clinical characteristics. Spatial distribution and temporal behaviour were also assessed, and a predictive time series model was created to estimate expected cases in the near future. Figures for resident foreign population, Spanish residents’ trips to endemic regions and the expansion of Aedes albopictus were also evaluated. Results A total of 588 dengue-related admissions were recorded: 49.6% were women, and the mean age was 34.3 years. One person died (0.2%), 82% presented with mild-to-moderate dengue and 7–8% with severe dengue. We observed a trend of steady and consistent increase in incidence (P < 0.05), in parallel with the increase in trips to dengue-endemic regions. Most admissions occurred during the summer, showing significant seasonality with 3-year peaks. We also found important regional differences. According to the predictive time series analysis, a continuing increase in imported dengue incidence can be expected in the near future, which, in the worst case scenario (upper 95% confidence interval), would mean an increase of 65% by 2025. Conclusion We present a nationwide study based on hospital, immigration, travel and entomological data. The constant increase in dengue-related hospitalizations, in combination with wider vector distribution, could imply a higher risk of autochthonous dengue transmission in the years to come. Strengthening the human and vector surveillance systems is a necessity, as are improvements in control measures, in the education of the general public and in fostering their collaboration in order to reduce the impact of imported dengue and to prevent the occurrence of autochthonous cases.

scholarly journals Potential spread and economic impact of invasive Drosophila suzukii in Brazil

2016 ◽  
Vol 51 (5) ◽  
pp. 571-578 ◽  
Author(s):  
Norton Polo Benito ◽  
Marcelo Lopes-da-Silva ◽  
Régis Sivori Silva dos Santos
Keyword(s):  
Thermal Stress ◽  
Economic Impact ◽  
Control Measures ◽  
Invasive Pest ◽  
Economic Losses ◽  
Drosophila Suzukii ◽  
Case Scenario ◽  
Worst Case ◽  
Thermal Requirements ◽  
Production Areas

Abstract: The objective of this work was to outline the potential distribution and economic impact of Drosophila suzukii (Diptera: Drosophilidae), a recent invasive pest, in Brazil. Two maps of the potential establishment of the species were drawn based on the ecoclimatic index (EI), which uses the following thermal requirements for the species: with thermal stress, most restrictive scenario for spread; and without thermal stress. The EI was classified into four ranges: unfavorable, ≤25%; less favorable, >25 to ≤50%; favorable, >50 to ≤75%; and highly favorable, >75%. Economic losses were estimated based on the most restrictive map. The highly favorable areas were overlapped with those of the maps of production data for each possible host (apple, grape, peach, persimmon, fig, and pear). Considering these six hosts, the overlap between the highly favorable and the production areas varied from 45.5% (grape) to 98.3% (apple). However, the monetary estimation of the potential losses in the worst case scenario (no control measures) was possible only for figs and peaches. Southern Brazil is the most climatically favorable area for D. suzukii development and where potential economic losses are expected to be the greatest. Maximum average temperatures (>30°C) are the main ecological factor to limit D. suzukii spread in Brazil.

scholarly journals Modelling the impact of a smallpox attack in India and influence of disease control measures

BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e038480
Author(s):  
Biswajit Mohanty ◽  
Valentina Costantino ◽  
Jai Narain ◽  
Abrar Ahmad Chughtai ◽  
Arpita Das ◽  
...  
Keyword(s):  
Management Strategies ◽  
Antiviral Agent ◽  
Control Measures ◽  
System Capacity ◽  
Secondary Outcome ◽  
Emerging Infections ◽  
Case Scenario ◽  
Worst Case ◽  
Ring Vaccination ◽  
The Impact

ObjectivesTo estimate the impact of a smallpox attack in Mumbai, India, examine the impact of case isolation and ring vaccination for epidemic containment and test the health system capacity under different scenarios with available interventions.SettingThe research is based on Mumbai, India population.InterventionsWe tested 50%, 70%, 90% of case isolation and contacts traced and vaccinated (ring vaccination) in the susceptible, exposed, infected, recovered model and varied the start of intervention between 20, 30 and 40 days after the initial attack.Primary and secondary outcome measuresWe estimated and incorporated in the model the effect of past vaccination protection, age-specific immunosuppression and contact rates and Mumbai population age structure in modelling disease morbidity and transmission.ResultsThe estimated duration of an outbreak ranged from 127 days to 8 years under different scenarios, and the number of vaccine doses needed for ring vaccination ranged from 16 813 to 8 722 400 in the best-case and worst-case scenarios, respectively. In the worst-case scenario, the available hospital beds in Mumbai would be exceeded. The impact of a smallpox epidemic may be severe in Mumbai, especially compared with high-income settings, but can be reduced with early diagnosis and rapid response, high rates of case finding and isolation and ring vaccination.ConclusionsThis study tells us that if smallpox re-emergence occurs, it may have significant health and economic impact, the extent of which will depend on the availability and delivery of interventions such as a vaccine or antiviral agent, and the capacity of case isolation and treatment. Further research on health systems requirements and capacity across the diverse states and territories of India could improve the preparedness and management strategies in the event of re-emergent smallpox or other serious emerging infections.

scholarly journals Updated COVID-19 Outbreak Forecast for San Diego County, April 2020

Outbreak ◽  
2020 ◽  
Author(s):  
Emily Eshraghian ◽  
Nathan Jacobs ◽  
Jeffrey Morgan
Keyword(s):  
San Diego ◽  
Weighted Average ◽  
San Diego County ◽  
Case Scenario ◽  
Worst Case ◽  
Social Distancing ◽  
Hospital Capacity ◽  
First Case ◽  
Scenario Model ◽  
Incident Cases

Here we extend and update our earlier projections of COVID-19 hospitalizations in San Diego County (1), and report a more optimistic outlook through the end of April 2020. San Diego confirmed its first case of COVID-19 on March 7, 2020. Several mitigation efforts were enacted on various dates, including a state-mandated stay-at-home order and enforcement of social distancing in public areas. Though mitigation strategies are helping lower the burden of disease, incident cases continue to increase exponentially. Our updated model includes data up to April 7 and does not forecast beyond April 30. Our approach uses a “wisdom of crowds” strategy (see link to methods for details) where a range of outbreak models from worst case scenario (Model A) to best case scenario (Model C) were presented to experts and non-experts (n=8) who were asked to vote on a most plausible model for expected COVID-19 spread. Final vote tallies were used to create a weighted average (Model M) as the official model projection. Our model predicts that San Diego County will not hit hospital capacity for standard hospital beds (panel a) nor for intensive care unit (ICU) beds (panel b) within April 2020. If current conditions continue, we predict the expected “surge” in hospitalizations to occur without surpassing hospital capacity, and that hospitalizations will decrease thereafter until the outbreak has been contained. However, it is important to note that factors such as changes in social distancing policies, even if occurring when existing or incident cases are low, may still result in new outbreaks and future spikes in hospitalizations. Furthermore, no models have been extensively validated for COVID-19. We encourage all residents of San Diego to continue rigorously following social distancing practices to improve the likelihood of best case scenarios and limit the scope of possible worst case scenarios.

scholarly journals Numerical Modeling of the Exposure on Radiofrequency Radiation of Marine Mammal Observers During Their Shift: A Case Study

2020 ◽  
Vol 10 (6) ◽  
pp. 753-758
Author(s):  
Christos C. Spandonidis ◽  
Kyriakoula Arvaniti
Keyword(s):  
Risk Assessment ◽  
Numerical Modeling ◽  
Radiation Exposure ◽  
Marine Mammal ◽  
Working Environment ◽  
Control Measures ◽  
Operational Environment ◽  
Exposure Limits ◽  
Case Scenario ◽  
Worst Case

Following the standard numerical modeling approach for Electromagnetic Field (EMF) radiation exposure prediction, we intend to provide an analytical framework to Marine Mammal Observers (MMOs) for dynamic risk assessment; enhancing thus occupational health and safety awareness. The analysis is based on power levels and antenna characteristics reported by MMOs for two systems (VHF and UHF) located close to the working environment. Whilst occupational exposure limits apply for MMOs, as for the rest crew (seismic and maritime), evaluation of exposure levels against general public limits is presented as well. At present we have restricted our study to single-source radiation, as well as we did not consider any irregularities due to system malfunction. The worst-case scenario of continuous RF transmission was considered. Risk assessment indicated regions where radiation exposure is higher than the permissible limits. Uncertainty due to the operational environment is inserted in methodology using an uncertainty coefficient. A list of control measures is proposed, to support both MMO’s and Operators’ decision making.

Forecasting the Domestic Utilization of Natural Gas in Nigeria (2015-2020)

2016 ◽  
Author(s):  
J. J. Adaji ◽  
R. U. Onolemhemhen ◽  
S. O. Isehunwa ◽  
A. Adenikinju
Keyword(s):  
Natural Gas ◽  
Alternative Fuels ◽  
Arima Model ◽  
Ordinary Least Squares ◽  
Case Scenario ◽  
Worst Case ◽  
Gas Utilization ◽  
Real Gdp ◽  
Resource Endowment ◽  
Utilization Patterns

ABSTRACT Domestic utilization of natural gas in Nigeria is being hampered by the poor developments in the natural gas sector over the years, with low level of electricity (generation) consumption per capital, weak legal, commercial and regulatory framework amidst poor infrastructural developments in natural gas as compared to that which exists for oil. Nigeria ranks the second in gas flaring and shows low volumes of domestic gas utilization, consuming only about 11% out of the 8.25 billion cubic feet produced per day in 2014 despite its natural gas resource endowment. This paper examines the determinants of domestic utilization of natural gas in Nigeria from 1990-2013. It investigates its relationship as a function of price of natural gas, price of alternative fuels, foreign direct investment, volumes of gas flared, electricity generated from natural gas sources and per capital real GDP. Going further, it forecasts its likely growth rate for a short-term period, using an econometric methodology of ordinary least squares and an ARIMA model, it estimates the relationship between the variables and uses the historical trend to forecast into the future. The result of the study showed that the determinants jointly explain the pattern of domestic gas utilization in Nigeria by 98%. Individually, per capital real GDP, electricity generated from natural gas sources and changes in the volume of domestic utilization of natural gas was found to have a positive and significant effect on domestic gas utilization. Further, the forecast values show evidence of a slow but gradual increase in utilization pattern in the near future from 2015-2020. A best-case scenario of an increase of 0.15% and a worst-case scenario of a decrease of 0.14% was presented. In conclusion, having identified significant influences on domestic gas utilization patterns in Nigeria it is imperative that the government uses economic instrument to enhance the utilization patterns in Nigeria by improving economic activities and developing the power sector which shows significant influence in domestic natural gas utilization patterns.

scholarly journals Short-Term Forecasts of COVID-19 Spread Across Indian States Until 1 May 2020

2020 ◽  
Author(s):  
Sarita Azad ◽  
Neeraj Poonia
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
The Other ◽  
Time Data ◽  
Short Term ◽  
Indian States ◽  
Autoregressive Integrated Moving Average ◽  
First Case ◽  
Real Time Data ◽  
Exponential Smoothing Method

The very first case of corona-virus illness was recorded on 30 January 2020, in India and the number of infected cases, including the death toll, continues to rise. In this paper, we present short-term forecasts of COVID-19 for 28 Indian states and five union territories using real-time data from 30 January to 21 April 2020. Applying Holt’s second-order exponential smoothing method and autoregressive integrated moving average (ARIMA) model, we generate 10-day ahead forecasts of the likely number of infected cases and deaths in India for 22 April to 1 May 2020. Our results show that the number of cumulative cases in India will rise to 36335.63 [PI 95% (30884.56, 42918.87)], concurrently the number of deaths may increase to 1099.38 [PI 95% (959.77, 1553.76)] by 1 May 2020. Further, we have divided the country into severity zones based on the cumulative cases. According to this analysis, Maharashtra is likely to be the most affected states with around 9787.24 [PI 95% (6949.81, 13757.06)] cumulative cases by 1 May 2020. However, Kerala and Karnataka are likely to shift from the red zone (i.e. highly affected) to the lesser affected region. On the other hand, Gujarat and Madhya Pradesh will move to the red zone. These results mark the states where lockdown by 3 May 2020, can be loosened.

scholarly journals Randomized Incremental Construction of Delaunay Triangulations of Nice Point Sets

2020 ◽  
Author(s):  
Jean-Daniel Boissonnat ◽  
Olivier Devillers ◽  
Kunal Dutta ◽  
Marc Glisse
Keyword(s):  
Delaunay Triangulation ◽  
Case Scenario ◽  
Worst Case ◽  
Geometric Data Structures ◽  
Delaunay Triangulations ◽  
Randomized Incremental Construction ◽  
First Case ◽  
Worst Case Complexity ◽  
Time Optimal ◽  
Incremental Construction

Abstract Randomized incremental construction (RIC) is one of the most important paradigms for building geometric data structures. Clarkson and Shor developed a general theory that led to numerous algorithms which are both simple and efficient in theory and in practice. Randomized incremental constructions are usually space-optimal and time-optimal in the worst case, as exemplified by the construction of convex hulls, Delaunay triangulations, and arrangements of line segments. However, the worst-case scenario occurs rarely in practice and we would like to understand how RIC behaves when the input is nice in the sense that the associated output is significantly smaller than in the worst case. For example, it is known that the Delaunay triangulation of nicely distributed points in $${\mathbb {E}}^d$$ E d or on polyhedral surfaces in $${\mathbb {E}}^3$$ E 3 has linear complexity, as opposed to a worst-case complexity of $$\Theta (n^{\lfloor d/2\rfloor })$$ Θ ( n ⌊ d / 2 ⌋ ) in the first case and quadratic in the second. The standard analysis does not provide accurate bounds on the complexity of such cases and we aim at establishing such bounds in this paper. More precisely, we will show that, in the two cases above and variants of them, the complexity of the usual RIC is $$O(n\log n)$$ O ( n log n ) , which is optimal. In other words, without any modification, RIC nicely adapts to good cases of practical value. At the heart of our proof is a bound on the complexity of the Delaunay triangulation of random subsets of $${\varepsilon }$$ ε -nets. Along the way, we prove a probabilistic lemma for sampling without replacement, which may be of independent interest.

scholarly journals Malaria epidemiology and economics: the effect of delayed immune acquisition on the cost–effectiveness of insecticide–treated bednets

1999 ◽  
Vol 354 (1384) ◽  
pp. 827-835 ◽  
Author(s):  
H. L. Guyatt ◽  
R. W. Snow ◽  
D. B. Evans
Keyword(s):  
Cost Effectiveness ◽  
Protective Efficacy ◽  
Control Measures ◽  
Attributable Mortality ◽  
Case Scenario ◽  
Worst Case ◽  
Mortality And Morbidity ◽  
Use Of Resources ◽  
Specific Mortality ◽  
Functional Immunity

An understanding of the epidemiology of a disease is central in evaluating the health impact and cost–effectiveness of control interventions. The epidemiology of life–threatening malaria is receiving renewed interest, with concerns that the implementation of preventive measures such as insecticide–treated bednets (ITNs) while protecting young children might in fact increase the risks of mortality and morbidity in older ages by delaying the acquisition of functional immunity. This paper aims to illustrate how a combined approach of epidemiology and economics can be used to (i) explore the long–term impact of changes in epidemiological profiles, and (ii) identify those variables that are critical in determining whether an intervention will be an efficient use of resources. The key parameters for determining effectiveness are the protective efficacy of ITNs (reduction in all–cause mortality), the malaria attributable mortality and the increased malaria–specific mortality risk due to delays in the acquisition of functional immunity. In particular, the analysis demonstrates that delayed immune acquisition is not a problem per se , but that the critical issue is whether it occurs immediately following the implementation of an ITN programme or whether it builds up slowly over time. In the ‘worst case’ scenario where ITNs immediately increase malaria–specific mortality due to reduced immunity, the intervention might actually cost lives. In other words, it might be better to not use ITNs. On the other hand, if reduced immunity takes two years to develop, ITNs would still fall into the category of excellent value for money compared to other health interventions, saving a year of life (YLL) at a cost of between US$25–30. These types of calculations are important in identifying the parameters which field researchers should be seeking to measure to address the important question of the net impact of delaying the acquisition of immunity through preventive control measures.

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