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Karasinghe N, Peiris S, Jayathilaka R, Dharmasena T. Forecasting weekly dengue incidence in Sri Lanka: Modified Autoregressive Integrated Moving Average modeling approach. PLoS One 2024; 19:e0299953. [PMID: 38457405 PMCID: PMC10923413 DOI: 10.1371/journal.pone.0299953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Accepted: 02/09/2024] [Indexed: 03/10/2024] Open
Abstract
Dengue poses a significant and multifaceted public health challenge in Sri Lanka, encompassing both preventive and curative aspects. Accurate dengue incidence forecasting is pivotal for effective surveillance and disease control. To address this, we developed an Autoregressive Integrated Moving Average (ARIMA) model tailored for predicting weekly dengue cases in the Colombo district. The modeling process drew on comprehensive weekly dengue fever data from the Weekly Epidemiological Reports (WER), spanning January 2015 to August 2020. Following rigorous model selection, the ARIMA (2,1,0) model, augmented with an autoregressive component (AR) of order 16, emerged as the best-fitted model. It underwent initial calibration and fine-tuning using data from January 2015 to August 2020, and was validated against independent 2000 data. Selection criteria included parameter significance, the Akaike Information Criterion (AIC), and Schwarz Bayesian Information Criterion (SBIC). Importantly, the residuals of the ARIMA model conformed to the assumptions of randomness, constant variance, and normality affirming its suitability. The forecasts closely matched observed dengue incidence, offering a valuable tool for public health decision-makers. However, an increased percentage error was noted in late 2020, likely attributed to factors including potential underreporting due to COVID-19-related disruptions amid rising dengue cases. This research contributes to the critical task of managing dengue outbreaks and underscores the dynamic challenges posed by external influences on disease surveillance.
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Affiliation(s)
| | - Sarath Peiris
- Department of Mathematics and Statistics, Faculty of Humanities and Sciences, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
| | - Ruwan Jayathilaka
- Department of Information Management, SLIIT Business School, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
| | - Thanuja Dharmasena
- National Coordinator, Global Environment Facility Small Grants Programme, UNDP, Colombo, Sri Lanka
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Ship Path Planning Based on Buoy Offset Historical Trajectory Data. JOURNAL OF MARINE SCIENCE AND ENGINEERING 2022. [DOI: 10.3390/jmse10050674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In the existing research on the intelligent navigation of ships, navigation route planning often regards light buoys as fixed obstructions. However, due to factors such as water ripples, the position of the buoys keeps periodically changing. If the buoys are set to a fixed range of avoidance areas in the process of ship navigation, it is easy to allow a collision between the ship and the light buoys. Therefore, based on historical motion trajectory data of the buoys, a SARIMA-based time-series prediction model is proposed to estimate the offset position of a given buoy in a specified time. Furthermore, the collision-free path planning approach is presented to dynamically recommend an accurate sailing path. The results of the simulation experiment show that this method can effectively deal with collisions of ships caused by the offset position of the light buoys during the navigation of the large and low-speed autonomous ships.
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Wang M, Zhang Q, Tai C, Li J, Yang Z, Shen K, Guo C. Design of PM2.5 monitoring and forecasting system for opencast coal mine road based on internet of things and ARIMA Mode. PLoS One 2022; 17:e0267440. [PMID: 35511915 PMCID: PMC9071147 DOI: 10.1371/journal.pone.0267440] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 04/09/2022] [Indexed: 12/23/2022] Open
Abstract
The dust produced by transportation roads is the primary source of PM2.5 pollution in opencast coal mines. However, China’s opencast coal mines lack an efficient and straightforward construction scheme of monitoring and management systems and a short-term prediction model to support dust control. In this study, by establishing a PM2.5 and other real-time environmental information to monitor, manage, visualize and predict the Internet of things monitoring and prediction system to solve these problems. This study solves these problems by establishing an Internet of things monitoring and prediction system, which can monitor PM2.5 and other real-time environmental information for monitoring, management, visualization, and prediction. We use Lua language to write interface protocol code in the APRUS adapter, which can simplify the construction of environmental monitoring system. The Internet of things platform has a custom visualization scheme, which is convenient for managers without programming experience to manage sensors and real-time data. We analyze real-time data using a time series model in Python, and RMSE and MAPE evaluate cross-validation results. The evaluation results show that the average RMSE of the ARIMA (4,1,0) and Double Exponential Smoothing models are 12.68 and 8.34, respectively. Both models have good generalization ability. The average MAPE of the fitting results are 10.5% and 1.7%, respectively, and the relative error is small. Because the ARIMA model has a more flexible prediction range and strong expansibility, and ARIMA model shows good adaptability in cross-validation, the ARIMA model is more suitable as the short-term prediction model of the prediction system. The prediction system can continuously predict PM2.5 dust through the ARIMA model. The monitoring and prediction system is very suitable for managers of opencast coal mines to prevent and control road dust.
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Affiliation(s)
- Meng Wang
- College of Mining Engineering, Liaoning Technical University, Fuxin, China
| | - Qiaofeng Zhang
- College of Mining Engineering, Liaoning Technical University, Fuxin, China
- * E-mail:
| | - Caiwang Tai
- College of Mining Engineering, Liaoning Technical University, Fuxin, China
| | - Jiazhen Li
- College of Mining Engineering, Liaoning Technical University, Fuxin, China
| | - Zongwei Yang
- College of Mining Engineering, Liaoning Technical University, Fuxin, China
| | - Kejun Shen
- College of Mining Engineering, Liaoning Technical University, Fuxin, China
| | - Chengbin Guo
- Shenzhen Mixlinker Networks Co., Ltd., Shenzhen, China
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Kim D, Kim SB, Jeon S, Kim S, Lee KH, Lee HS, Han SH. No Change of Pneumocystis jirovecii Pneumonia after the COVID-19 Pandemic: Multicenter Time-Series Analyses. J Fungi (Basel) 2021; 7:jof7110990. [PMID: 34829277 PMCID: PMC8624436 DOI: 10.3390/jof7110990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/08/2021] [Accepted: 11/17/2021] [Indexed: 11/30/2022] Open
Abstract
Consolidated infection control measures imposed by the government and hospitals during COVID-19 pandemic resulted in a sharp decline of respiratory viruses. Based on the issue of whether Pneumocystis jirovecii could be transmitted by airborne and acquired from the environment, we assessed changes in P. jirovecii pneumonia (PCP) cases in a hospital setting before and after COVID-19. We retrospectively collected data of PCP-confirmed inpatients aged ≥18 years (N = 2922) in four university-affiliated hospitals between January 2015 and June 2021. The index and intervention dates were defined as the first time of P. jirovecii diagnosis and January 2020, respectively. We predicted PCP cases for post-COVID-19 and obtained the difference (residuals) between forecasted and observed cases using the autoregressive integrated moving average (ARIMA) and the Bayesian structural time-series (BSTS) models. Overall, the average of observed PCP cases per month in each year were 36.1 and 47.3 for pre- and post-COVID-19, respectively. The estimate for residuals in the ARIMA model was not significantly different in the total PCP-confirmed inpatients (7.4%, p = 0.765). The forecasted PCP cases by the BSTS model were not significantly different from the observed cases in the post-COVID-19 (−0.6%, 95% credible interval; −9.6~9.1%, p = 0.450). The unprecedented strict non-pharmacological interventions did not affect PCP cases.
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Affiliation(s)
- Dayeong Kim
- Department of Internal Medicine, Division of Infectious Disease, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul 06273, Korea; (D.K.); (S.K.); (K.H.L.)
| | - Sun Bean Kim
- Department of Internal Medicine, Division of Infectious Diseases, Korea University College of Medicine, 73, Goryeodae-ro, Seongbuk-gu, Seoul 02841, Korea;
| | - Soyoung Jeon
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul 06273, Korea;
| | - Subin Kim
- Department of Internal Medicine, Division of Infectious Disease, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul 06273, Korea; (D.K.); (S.K.); (K.H.L.)
| | - Kyoung Hwa Lee
- Department of Internal Medicine, Division of Infectious Disease, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul 06273, Korea; (D.K.); (S.K.); (K.H.L.)
| | - Hye Sun Lee
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul 06273, Korea;
- Correspondence: (H.S.L.); (S.H.H.)
| | - Sang Hoon Han
- Department of Internal Medicine, Division of Infectious Disease, Yonsei University College of Medicine, 211 Eonju-ro, Gangnam-gu, Seoul 06273, Korea; (D.K.); (S.K.); (K.H.L.)
- Correspondence: (H.S.L.); (S.H.H.)
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Navarro Valencia V, Díaz Y, Pascale JM, Boni MF, Sanchez-Galan JE. Assessing the Effect of Climate Variables on the Incidence of Dengue Cases in the Metropolitan Region of Panama City. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182212108. [PMID: 34831862 PMCID: PMC8619576 DOI: 10.3390/ijerph182212108] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 11/24/2022]
Abstract
The present analysis uses the data of confirmed incidence of dengue cases in the metropolitan region of Panama from 1999 to 2017 and climatic variables (air temperature, precipitation, and relative humidity) during the same period to determine if there exists a correlation between these variables. In addition, we compare the predictive performance of two regression models (SARIMA, SARIMAX) and a recurrent neural network model (RNN-LSTM) on the dengue incidence series. For this data from 1999–2014 was used for training and the three subsequent years of incidence 2015–2017 were used for prediction. The results show a correlation coefficient between the climatic variables and the incidence of dengue were low but statistical significant. The RMSE and MAPE obtained for the SARIMAX and RNN-LSTM models were 25.76, 108.44 and 26.16, 59.68, which suggest that any of these models can be used to predict new outbreaks. Although, it can be said that there is a limited role of climatic variables in the outputs the models. The value of this work is that it helps understand the behaviour of cases in a tropical setting as is the Metropolitan Region of Panama City, and provides the basis needed for a much needed early alert system for the region.
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Affiliation(s)
- Vicente Navarro Valencia
- Facultad de Ciencias y Tecnología, Universidad Tecnológica de Panamá (UTP), El Dorado 0819-07289, Panama;
| | - Yamilka Díaz
- Department of Research in Virology and Biotechnology, Gorgas Memorial Institute of Health Studies, Justo Arosemena Avenue and 35st Street, Panama 0816-02593, Panama;
| | - Juan Miguel Pascale
- Unit of Diagnosis, Clinical Research and Tropical Medicine, Gorgas Memorial Institute of Health Studies, Justo Arosemena Avenue and 35st Street, Panama 0816-02593, Panama;
- Sistema Nacional de Investigación (SNI) SENACYT, Panama 0816-02852, Panama
| | - Maciej F. Boni
- Center for Infectious Disease Dynamics, Department of Biology, Pennsylvania State University, University Park, PA 16802, USA;
| | - Javier E. Sanchez-Galan
- Facultad de Ciencias y Tecnología, Universidad Tecnológica de Panamá (UTP), El Dorado 0819-07289, Panama;
- Sistema Nacional de Investigación (SNI) SENACYT, Panama 0816-02852, Panama
- Grupo de Investigaciones en Biotecnología, Bioinformática y Biología de Sistemas (GIBBS), Facultad de Ingenieria de Sistemas Computacionales, Universidad Tecnológica de Panamá (UTP), El Dorado 0819-07289, Panama
- Correspondence: ; Tel.: +507-560-3933
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Zheng Y, Zhang L, Wang C, Wang K, Guo G, Zhang X, Wang J. Predictive analysis of the number of human brucellosis cases in Xinjiang, China. Sci Rep 2021; 11:11513. [PMID: 34075198 PMCID: PMC8169839 DOI: 10.1038/s41598-021-91176-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 05/24/2021] [Indexed: 02/04/2023] Open
Abstract
Brucellosis is one of the major public health problems in China, and human brucellosis represents a serious public health concern in Xinjiang and requires a prediction analysis to help making early planning and putting forward science preventive and control countermeasures. According to the characteristics of the time series of monthly reported cases of human brucellosis in Xinjiang from January 2008 to June 2020, we used seasonal autoregressive integrated moving average (SARIMA) method and nonlinear autoregressive regression neural network (NARNN) method, which are widely prevalent and have high prediction accuracy, to construct prediction models and make prediction analysis. Finally, we established the SARIMA((1,4,5,7),0,0)(0,1,2)12 model and the NARNN model with a time lag of 5 and a hidden layer neuron of 10. Both models have high fitting performance. After comparing the accuracies of two established models, we found that the SARIMA((1,4,5,7),0,0)(0,1,2)12 model was better than the NARNN model. We used the SARIMA((1,4,5,7),0,0)(0,1,2)12 model to predict the number of monthly reported cases of human brucellosis in Xinjiang from July 2020 to December 2021, and the results showed that the fluctuation of the time series from July 2020 to December 2021 was similar to that of the last year and a half while maintaining the current prevention and control ability. The methodology applied here and its prediction values of this study could be useful to give a scientific reference for prevention and control human brucellosis.
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Affiliation(s)
- Yanling Zheng
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830054, People's Republic of China.
| | - Liping Zhang
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830054, People's Republic of China
| | - Chunxia Wang
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830054, People's Republic of China
| | - Kai Wang
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830054, People's Republic of China
| | - Gang Guo
- State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Clinical Medicine Institute, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People's Republic of China
| | - Xueliang Zhang
- College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830054, People's Republic of China.
| | - Jing Wang
- Department of Respiratory Medicine, First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, People's Republic of China.
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