Weather integrated malaria prediction system using Bayesian structural time series model for northeast states of India

Vavilala, Hariprasad; Yaladanda, Nikhila; Kondeti, Phani Krishna; Unissa, Rafiq; Mopuri, Rajasekhar; Gouda, K. C.; Bhimala, Kantha Rao; Kadiri, Madhusudhan Rao; Upadhyayula, Suryanaryana Murty; Mutheneni, Srinivasa Rao

doi:10.1007/s11356-022-20642-y

Cited by 7 publications

(5 citation statements)

References 44 publications

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“…There are several indicators that climate change would influence both the vectors and the pathogens. Higher humidity and temperatures generally favor mosquito abundance, but hot and arid areas may reduce it 36–38 . Factors driving malaria transmission in urban areas may differ from those in rural areas (Table S1).…”

Section: Resultsmentioning

confidence: 99%

“…Higher humidity and temperatures generally favor mosquito abundance, but hot and arid areas may reduce it. [36][37][38] Factors driving malaria transmission in urban areas may differ from those in rural areas (Table S1). The impact of climate change on malaria may also be limited by public health control activities.…”

Section: Long-term Climate and Habitat Changes That Are Associated Wi...mentioning

confidence: 99%

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Climate change and parasitic risk to the blood supply

et al. 2022

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Section: Resultsmentioning

confidence: 99%

Section: Long-term Climate and Habitat Changes That Are Associated Wi...mentioning

confidence: 99%

Climate change and parasitic risk to the blood supply

et al. 2022

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“…Weather-integrated infectious disease prediction models predominantly include ARDL [21], generalized linear models [30], Bayesian structural time series [31], and ARIMA [32]. In comparison to the aforementioned models, the NARDL offers several advantages in modeling HB incidence series [14][15][16]33]: (1) NARDL can account for cases where the impact of positive changes in weather factors differs from the impact of negative changes; (2) by including lagged values of variables in the model, NARDL enables the examination of both immediate and persistent effects of weather factors, contributing to a more comprehensive analysis; and (3) NARDL allows for straightforward interpretation of coefficients, making it possible to capture the direction and magnitude of the effects of weather factors.…”

Section: Discussionmentioning

confidence: 99%

Asymmetric Effects of Weather-Integrated Human Brucellosis Forecasting System Using a New Nonlinear Autoregressive Distributed Lag Model

Wang,

Xue,

Zhang

et al. 2024

Transboundary and Emerging Diseases

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Human brucellosis (HB) remains a significant public health concern in China. This study aimed to investigate the long- and short-term asymmetric impacts of meteorological variables on HB and develop an early prediction system. Monthly data on HB incidence and meteorological variables were collected from 2005 to 2020. The study employed the autoregressive distributed lag (ARDL) and nonlinear ARDL (NARDL) to analyze the long- and short-term effects of climate variables on HB. Subsequently, the data were split into training (from January 2005 to December 2019) and testing parts (from January to December 2020) to develop and validate the forecasting accuracy of both models. During 2005–2020, there were 34,993 HB cases (2.03 per 100,000 persons) and there was an overall rising trend (average annual percentage change = 21.18%, 95%CI 18.36%–26.01%) in HB incidence, peaked in May and troughed in December per year. A 1 m/s increment and decrement in differenced (Δ) average wind velocity (AWV) contributed to 73.8% and 87.5% increases in ΔHB incidence, respectively (Wald long-run asymmetry test (WLR) = 1.17, P=0.25). A 1 hr increment and decrement in Δ(average relative humidity) contributed to both 3.1% increases in ΔHB incidence (Wald short-run asymmetry test = 3.01, P=0.003). Average temperature (AT) (P<0.001) and average air pressure (P=0.012) played a long-run linear impact on HB. Δ(aggregate precipitation) (WLR = 1.76, P=0.08) and Δ(aggregate sunshine hours) (WLR = 0.07, P=0.94) did not have a significant long-term asymmetric impact on Δlog(HB). ΔΔAT(+) and ΔΔAWV(−) at a 1-month lag had a meaningful short-run effect on Δlog(HB). In the forecasting aspect, the NARDL produced significantly smaller error rates compared to the ARDL. Weather variability played significant long- and short-run asymmetric roles in HB incidence. The NARDL by integrating climatic variables could accurately capture the dynamic structure of HB epidemic, meaning that meteorological variables should be integrated into the public health intervention plan for HB.

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“…The mathematically form the ARIMA (p, d, q) model can be written as: Importantly, the predictions made using the BSTS method rarely depend on speci c hypothesized speci cations. The forecast generated by the BSTS model is based on prior information and the likelihood function, which are combined to produce a posterior distribution (11). A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the posterior distribution, and the sampling results are then averaged to obtain the nal prediction (10,13).…”

Section: Methodsmentioning

confidence: 99%

Evaluation of the impact of COVID-19 on hepatitis B in Henan Province and its epidemic trend based on Bayesian structured time series model

Li,

et al. 2024

Preprint

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Background There may be evidence that COVID-19 affects illness patterns. This study aimed to estimate epidemiological trends in China and to assess the effects of COVID-19 epidemic on the declines in hepatitis B (HB) case notifications. Methods The Bayesian structured time series (BSTS) method was used to investigate the causal effect of COVID-19 on the decline in HB cases based on the monthly incidence of HB from January 2013 to September 2022. To assess how well the BSTS algorithm performs predictions, we split the observations into various training and testing ranges. Results The incidence of HB in Henan was generally declining with periodicity and seasonality. The seasonal index in September and February was the smallest (0.91 and 0.93), and that in March was the largest (1.19). Due to the COVID-19 pandemic, the monthly average number of notifications of HB cases decreased by 38% (95% credible intervals [CI]: -44% ~ -31%) from January to March 2020, by 24% (95% CI: -29% ~ -17%) from January to June 2020, by 15% (95% CI: -19% ~ -9.2%) from January to December 2020, by 11% (95% CI: -15% ~ -6.7%) from January 2020 to June 2021, and by 11% (95% CI: -15% ~ -7.3%) from January 2020 to December 2021. From January 2020 to September 2022, it decreased by 12% (95% CI: -16% ~ -8.1%). From 2021 to 2022, the impact of COVID-19 on HB was attenuated. In both training and test sets, the average absolute percentage error (10.03%) generated by the BSTS model was smaller than that generated by the ARIMA model (14.4%). It was also found that the average absolute error, root mean square error, and root mean square percentage error generated by the BSTS model were smaller than ones generated by the ARIMA model. The trend of HB cases in Henan from October 2022 to December 2023 predicted by the BSTS model remained stable, with a total number of 81,650 cases (95% CI: 47,372 ~ 115,391). Conclusions After COVID-19 intervention, the incidence of HB in Henan decreased and exhibited clear seasonal and cyclical trends. The BSTS model outperformed the ARIMA model in predicting the HB incidence trend in Henan. This information may serve as a reference and provide technical assistance for developing strategies and actions to prevent and control HB. Take additional measures to accelerate the progress of eliminating HB.

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Weather integrated malaria prediction system using Bayesian structural time series model for northeast states of India

Cited by 7 publications

References 44 publications

Climate change and parasitic risk to the blood supply

Climate change and parasitic risk to the blood supply

Asymmetric Effects of Weather-Integrated Human Brucellosis Forecasting System Using a New Nonlinear Autoregressive Distributed Lag Model

Evaluation of the impact of COVID-19 on hepatitis B in Henan Province and its epidemic trend based on Bayesian structured time series model

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