OBJECTIVE: To study the number of leptospirosis cases in relations to the seasonal pattern, and its association with climate factors. METHODS: Time series analysis was used to study the time variations in the number of leptospirosis cases. The Autoregressive Integrated Moving Average (ARIMA) model was used in data curve fitting and predicting the next leptospirosis cases. RESULTS: We found that the amount of rainfall was correlated to leptospirosis cases in both regions of interest, namely the northern and northeastern region of Thailand, while the temperature played a role in the northeastern region only. The use of multivariate ARIMA (ARIMAX) model showed that factoring in rainfall (with an 8 months lag) yields the best model for the northern region while the model, which factors in rainfall (with a 10 months lag) and temperature (with an 8 months lag) was the best for the northeastern region. CONCLUSION: The models are able to show the trend in leptospirosis cases and closely fit the recorded data in both regions. The models can also be used to predict the next seasonal peak quite accurately.
OBJECTIVE: To study the number of leptospirosis cases in relations to the seasonal pattern, and its association with climate factors. METHODS: Time series analysis was used to study the time variations in the number of leptospirosis cases. The Autoregressive Integrated Moving Average (ARIMA) model was used in data curve fitting and predicting the next leptospirosis cases. RESULTS: We found that the amount of rainfall was correlated to leptospirosis cases in both regions of interest, namely the northern and northeastern region of Thailand, while the temperature played a role in the northeastern region only. The use of multivariate ARIMA (ARIMAX) model showed that factoring in rainfall (with an 8 months lag) yields the best model for the northern region while the model, which factors in rainfall (with a 10 months lag) and temperature (with an 8 months lag) was the best for the northeastern region. CONCLUSION: The models are able to show the trend in leptospirosis cases and closely fit the recorded data in both regions. The models can also be used to predict the next seasonal peak quite accurately.
Authors: Betrand Msemwa; Mariam M Mirambo; Vitus Silago; Juma M Samson; Khadija S Majid; Ginethon Mhamphi; Joseph Genchwere; Subira S Mwakabumbe; Elifuraha B Mngumi; Georgies Mgode; Stephen E Mshana Journal: Pathogens Date: 2021-05-16
Authors: Marie Hellung Schønning; Matthew David Phelps; Janith Warnasekara; Suneth B Agampodi; Peter Furu Journal: Ecohealth Date: 2019-11-09 Impact factor: 4.464
Authors: Claudia Muñoz-Zanzi; Meghan R Mason; Carolina Encina; Angel Astroza; Alex Romero Journal: Int J Environ Res Public Health Date: 2014-06-26 Impact factor: 3.390