Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTMResearch in context
Summary: Background: Coccidioidomycosis (CM), also known as Valley fever, is a respiratory infection. Recently, the number of confirmed cases of CM has been increasing. Precisely defining the influential factors and forecasting future infection can assist in public health messaging and treatment de...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | The Lancet Regional Health. Americas |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667193X25000201 |
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| author | Xueting Jin Fangwu Wei Srinivasa Srivatsav Kandala Tejas Umesh Kayleigh Steele John N. Galgiani Manfred D. Laubichler |
| author_facet | Xueting Jin Fangwu Wei Srinivasa Srivatsav Kandala Tejas Umesh Kayleigh Steele John N. Galgiani Manfred D. Laubichler |
| author_sort | Xueting Jin |
| collection | DOAJ |
| description | Summary: Background: Coccidioidomycosis (CM), also known as Valley fever, is a respiratory infection. Recently, the number of confirmed cases of CM has been increasing. Precisely defining the influential factors and forecasting future infection can assist in public health messaging and treatment decisions. Methods: We utilized Long Short-Term Memory (LSTM) networks to forecast CM cases, based on the daily pneumonia cases in Maricopa County, Arizona from 2020 to 2022. Besides weather and climate variables, we examined the impact of people's lifestyle change during COVID-19. Factors, including temperature, precipitation, wind speed, PM10 and PM2.5 concentration, drought, and stringency index, were included in LSTM networks, considering their association with CM prevalence, time-lag effect, and correlation with other factors. Findings: LSTM can predict CM prevalence with accurate trend and low mean squared error (MSE). We also found a tradeoff between the length of the forecasting period and the performance of the forecasting model. The models with longer forecasting periods have less accurate trends over time and higher MSEs. Two models with different lengths of forecasting periods, 10 days and 30 days, are identified with good prediction. Interpretation: LSTM algorithms, combined with traditional statistical methods, could help with the forecasting of CM cases. By predicting the CM prevalence, our results can inform researchers, epidemiologists, clinicians, and the public in order to assist public health. Funding: “Getting to the Source of Arizona's Valley Fever Problem: A Tri-University Collaboration to Map and Characterize the Pathogen Where It Grows” funded by the Arizona Board of Regents. |
| format | Article |
| id | doaj-art-5e59b9ec309a42c2832dc88c1d29e005 |
| institution | Kabale University |
| issn | 2667-193X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | The Lancet Regional Health. Americas |
| spelling | doaj-art-5e59b9ec309a42c2832dc88c1d29e0052025-02-06T05:13:01ZengElsevierThe Lancet Regional Health. Americas2667-193X2025-03-0143101010Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTMResearch in contextXueting Jin0Fangwu Wei1Srinivasa Srivatsav Kandala2Tejas Umesh3Kayleigh Steele4John N. Galgiani5Manfred D. Laubichler6Decision Theater, Knowledge Enterprise, Arizona State University, Tempe, AZ, USA; School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, USADecision Theater, Knowledge Enterprise, Arizona State University, Tempe, AZ, USA; Corresponding author. Arizona State University, Tempe, AZ, USA.Decision Theater, Knowledge Enterprise, Arizona State University, Tempe, AZ, USABarrow Neurological Institute, St Joseph's Hospital, Phoenix, AZ, USADecision Theater, Knowledge Enterprise, Arizona State University, Tempe, AZ, USAValley Fever Center for Excellence and the Departments of Medicine and Immunobiology, University of Arizona College of Medicine-Tucson, Tucson, AZ, USA; BIO5 Institute, University of Arizona, Tucson, AZ, USADecision Theater, Knowledge Enterprise, Arizona State University, Tempe, AZ, USASummary: Background: Coccidioidomycosis (CM), also known as Valley fever, is a respiratory infection. Recently, the number of confirmed cases of CM has been increasing. Precisely defining the influential factors and forecasting future infection can assist in public health messaging and treatment decisions. Methods: We utilized Long Short-Term Memory (LSTM) networks to forecast CM cases, based on the daily pneumonia cases in Maricopa County, Arizona from 2020 to 2022. Besides weather and climate variables, we examined the impact of people's lifestyle change during COVID-19. Factors, including temperature, precipitation, wind speed, PM10 and PM2.5 concentration, drought, and stringency index, were included in LSTM networks, considering their association with CM prevalence, time-lag effect, and correlation with other factors. Findings: LSTM can predict CM prevalence with accurate trend and low mean squared error (MSE). We also found a tradeoff between the length of the forecasting period and the performance of the forecasting model. The models with longer forecasting periods have less accurate trends over time and higher MSEs. Two models with different lengths of forecasting periods, 10 days and 30 days, are identified with good prediction. Interpretation: LSTM algorithms, combined with traditional statistical methods, could help with the forecasting of CM cases. By predicting the CM prevalence, our results can inform researchers, epidemiologists, clinicians, and the public in order to assist public health. Funding: “Getting to the Source of Arizona's Valley Fever Problem: A Tri-University Collaboration to Map and Characterize the Pathogen Where It Grows” funded by the Arizona Board of Regents.http://www.sciencedirect.com/science/article/pii/S2667193X25000201CoccidioidomycosisValley feverDeep learningLSTMTime series forecasting |
| spellingShingle | Xueting Jin Fangwu Wei Srinivasa Srivatsav Kandala Tejas Umesh Kayleigh Steele John N. Galgiani Manfred D. Laubichler Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTMResearch in context The Lancet Regional Health. Americas Coccidioidomycosis Valley fever Deep learning LSTM Time series forecasting |
| title | Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTMResearch in context |
| title_full | Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTMResearch in context |
| title_fullStr | Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTMResearch in context |
| title_full_unstemmed | Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTMResearch in context |
| title_short | Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTMResearch in context |
| title_sort | time series forecasting of valley fever infection in maricopa county az using lstmresearch in context |
| topic | Coccidioidomycosis Valley fever Deep learning LSTM Time series forecasting |
| url | http://www.sciencedirect.com/science/article/pii/S2667193X25000201 |
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