scholarly journals Epidemic prediction of dengue fever based on vector compartment model and Markov chain Monte Carlo method

2021 ◽  
Vol 22 (S5) ◽  
Author(s):  
Chien-Hung Lee ◽  
Ko Chang ◽  
Yao-Mei Chen ◽  
Jinn-Tsong Tsai ◽  
Yenming J. Chen ◽  
...  
Keyword(s):  
Markov Chain ◽  
Dengue Fever ◽  
Time Series Data ◽  
Compartment Model ◽  
Future Trend ◽  
Control Measures ◽  
Series Data ◽  
Time Data ◽  
Epidemic Prediction ◽  
And Control

Abstract Background Dengue epidemics is affected by vector-human interactive dynamics. Infectious disease prevention and control emphasize the timing intervention at the right diffusion phase. In such a way, control measures can be cost-effective, and epidemic incidents can be controlled before devastated consequence occurs. However, timing relations between a measurable signal and the onset of the pandemic are complex to be discovered, and the typical lag period regression is difficult to capture in these complex relations. This study investigates the dynamic diffusion pattern of the disease in terms of a probability distribution. We estimate the parameters of an epidemic compartment model with the cross-infection of patients and mosquitoes in various infection cycles. We comprehensively study the incorporated meteorological and mosquito factors that may affect the epidemic of dengue fever to predict dengue fever epidemics. Results We develop a dual-parameter estimation algorithm for a composite model of the partial differential equations for vector-susceptible-infectious-recovered with exogeneity compartment model, Markov chain Montel Carlo method, and boundary element method to evaluate the epidemic periodicity under the effect of environmental factors of dengue fever, given the time series data of 2000–2016 from three cities with a population of 4.7 million. The established computer model of “energy accumulation-delayed diffusion-epidemics” is proven to be effective to predict the future trend of reported and unreported infected incidents. Our artificial intelligent algorithm can inform the authority to cease the larvae at the highest vector infection time. We find that the estimated dengue report rate is about 20%, which is close to the number of official announcements, and the percentage of infected vectors increases exponentially yearly. We suggest that the executive authorities should seriously consider the accumulated effect among infected populations. This established epidemic prediction model of dengue fever can be used to simulate and evaluate the best time to prevent and control dengue fever. Conclusions Given our developed model, government epidemic prevention teams can apply this platform before they physically carry out the prevention work. The optimal suggestions from these models can be promptly accommodated when real-time data have been continuously corrected from clinics and related agents.

Knowledge and Awareness of Dengue Fever

2018 ◽  
pp. 219-222
Author(s):  
Y. Arockia Suganthi ◽  
Chitra K. ◽  
J. Magelin Mary
Keyword(s):  
Dengue Fever ◽  
Prevention And Control ◽  
Control Measures ◽  
Infection Transmission ◽  
Standard Guideline ◽  
The World ◽  
Different Types ◽  
Prevention And Control Measures ◽  
Dengue Prevention ◽  
And Control

Dengue fever is a painful mosquito-borne infection caused by different types of virus in various localities of the world. There is no particular medicine or vaccine to treat person suffering from dengue fever. Dengue viruses are transmitted by the bite of female Aedes (Ae) mosquitoes. Dengue fever viruses are mainly transmitted by Aedes which can be active in tropical or subtropical climates. Aedes Aegypti is the key step to avoid infection transmission to save millions of people in all over the world. This paper provides a standard guideline in the planning of dengue prevention and control measures. At the same time gives the priorities including clinical management and hospitalized dengue patients have to address essentially.

scholarly journals Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 540
Author(s):  
Fabio Amaral ◽  
Wallace Casaca ◽  
Cassio M. Oishi ◽  
José A. Cuminato
Keyword(s):  
Learning Strategies ◽  
Time Series Data ◽  
Sao Paulo ◽  
The State ◽  
Data Driven ◽  
Series Data ◽  
São Paulo ◽  
Time Data ◽  
Unified Framework ◽  
Infected People

São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given.

scholarly journals VN-INDEX TREND PREDICTION USING LONG-SHORT TERM MEMORY NEURAL NETWORKS

2019 ◽  
Vol 17 (12.2) ◽  
pp. 61
Author(s):  
Nguyen Ngoc Tra ◽  
Ho Phuoc Tien ◽  
Nguyen Thanh Dat ◽  
Nguyen Ngoc Vu
Keyword(s):  
Time Series Data ◽  
Short Term Memory ◽  
Moving Average ◽  
Future Trend ◽  
Series Data ◽  
Short Term ◽  
Trend Prediction ◽  
Temporal Dependence ◽  
Term Memory ◽  
Long Short Term Memory

The paper attemps to forecast the future trend of Vietnam index (VN-index) by using long-short term memory (LSTM) networks. In particular, an LSTM-based neural network is employed to study the temporal dependence in time-series data of past and present VN index values. Empirical forecasting results show that LSTM-based stock trend prediction offers an accuracy of about 60% which outperforms moving-average-based prediction.

Modelling the Nexus Between Foreign Aid and Economic Growth: A Case of Afghanistan and Egypt

2021 ◽  
Vol 39 (2) ◽  
Author(s):  
Muhammed Ashiq Villanthenkodath ◽  
Ubaid Mushtaq
Keyword(s):  
Economic Growth ◽  
Foreign Aid ◽  
Time Series Data ◽  
Trade Openness ◽  
Political Stability ◽  
Series Data ◽  
Long Run ◽  
Short Run ◽  
Annual Time Series ◽  
And Control

This paper tries to explore the existence of a long-run relationship between foreign aid and economic growth by using the data from the two highest foreign aid recipient countries. Using the annual time series data from 1965 to 2017 this study uses several econometric models such as Johansen and Juselius cointegration, Granger causality and vector auto regression to establish the long and short-run relationships among foreign aid inflows and economic growth while also considering financial development and trade openness from both the countries. The empirical results suggest that no long-run relationship exists among foreign aid inflows and economic growth for both the countries. However, unidirectional causality running from foreign aid to economic growth is indicative in both countries. Therefore, the findings in this paper support the adequate need for foreign aid for effective economic growth amid an upright policy environment, related issues of conditionality and political stability. Our results are robust to independent, and control variables and estimation techniques are also on par with robustness.

scholarly journals Study on Fractal Multistep Forecast for the Prediction of Driving Behavior

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Longhai Yang ◽  
Hong Xu ◽  
Xiqiao Zhang ◽  
Shuai Li ◽  
Wenchao Ji
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Time Series Data ◽  
Back Propagation ◽  
Series Data ◽  
Vehicle Speed ◽  
Trajectory Data ◽  
Time Data ◽  
Characteristic Analysis ◽  
Real Time Data

The application and development of new technology make it possible to acquire real-time data of vehicles. Based on these real-time data, the behavior of vehicles can be analyzed. The prediction of vehicle behavior provides data support for the fine management of traffic. This paper proposes speed and acceleration have fractal features by R/S analysis of the time series data of speed and acceleration. Based on the characteristic analysis of microscopic parameters, the characteristic indexes of parameters are quantified, the fractal multistep prediction model of microparameters is established, and the BP (back propagation neural networks) model is established to estimate predictable step of fractal prediction model. The fractal multistep prediction model is used to predict speed acceleration in the predictable step. NGSIM trajectory data are used to test the multistep prediction model. The results show that the proposed fractal multistep prediction model can effectively realize the multistep prediction of vehicle speed.

scholarly journals Autochthonous chikungunya and dengue fever outbreak in Mainland France, 2010-2018

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
F Franke ◽  
S Giron ◽  
A Cochet ◽  
C Jeannin ◽  
I Leparc-Goffart ◽  
...  
Keyword(s):  
Dengue Fever ◽  
Urban Areas ◽  
Control Measures ◽  
Entomological Surveillance ◽  
Awareness Campaigns ◽  
Investigation Methods ◽  
Imported Cases ◽  
Set Up ◽  
And Control ◽  
Autochthonous Transmission

Abstract Background Aedes albopictus, vector of dengue and chikungunya viruses, is implanted in mainland France, exposing to the risk of autochthonous transmission. Since 2006, epidemiological and entomological surveillance activities aim to prevent or limit the occurrence of autochthonous cases. We aimed to describe episodes of transmission and control measures implemented in order to reflect on surveillance activities. Methods We reviewed all publications and documents produced on autochthonous transmission episodes in France and surveillance protocols. We reviewed surveillance activities, investigation methods and control measures implemented. Results Between 2010 and 2018, eight episodes of autochthonous dengue fever transmission and three of chikungunya were recorded in mainland France. All of them occurred in the South east of France, between July and October, when vector density was the highest. Transmission areas were limited to single domestic houses located in discontinuous urban areas. Only two episodes happened in two distinct areas. Chikungunya episodes led to 31 cases and dengue fever episodes to 23 cases. Most cases were identified by door-to-door investigations set-up in transmission areas. We isolated serotypes 1 and 2 for dengue and East Central South Africa lineage for chikungunya in autochthonous cases. Adulticide vector control measures were effective in controlling transmission. Seven episodes of transmission were due to failure in identifying primary imported cases. Four episodes occurred because of the absence or the lack of vector controls measures around primary imported cases. Conclusions Surveillance activities, and autochthonous cases investigations, were effective in limiting the extent of transmission, but were highly demanding for surveillance actors. Identified causes of transmission highlight the need of regular awareness campaigns targeting physicians and biologists. Key messages Effectiveness of the surveillance system of dengue, chikungunya and zika viruses, and autochthonous cases investigations. Needs of awareness and training courses targeting health professionals to the risk represented by these viruses.

scholarly journals Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network

2017 ◽  
Vol 145 (6) ◽  
pp. 1118-1129 ◽  
Author(s):  
K. W. WANG ◽  
C. DENG ◽  
J. P. LI ◽  
Y. Y. ZHANG ◽  
X. Y. LI ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Prevention And Control ◽  
Time Series Data ◽  
Arima Model ◽  
Public Health Problem ◽  
Series Data ◽  
Percentage Error ◽  
And Control

SUMMARYTuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

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