Borrowing Data From Other Populations to Forecast Epidemic Size
- 1Department of Biological and Environmental Sciences University of Stirling Stirling UK
Abstract
Forecasting infectious disease dynamics is important for ecological management and emerging disease preparedness, yet many systems lack the long‐term datasets required to develop reliable predictions. Spatial replication may provide an alternative by allowing information to be shared among populations, but the extent to which this improves forecasting remains unclear. Here, I used epidemic and temperature data from 20 replicated semi‐natural Daphnia –parasite pond populations monitored across four seasons (80 epidemics) to test whether information from multiple populations improves forecasts and whether environmental similarity identifies informative populations. I forecasted disease prevalence, infected host density, and healthy host density using a benchmark model, autoregressive integrated moving average (ARIMA) and time‐series regression models. Models were trained using focal‐population data alone, mean dynamics across other populations, or temperature‐weighted mean dynamics from environmentally similar populations. Epidemics showed strong seasonal structure, with prevalence and infected host density generally peaking during the warmest period of the season. However, no forecasting approach consistently outperformed others across all variables. Temperature improved forecasts of prevalence and infected host density but not healthy host density. Multi‐population training improved forecasts in some cases, particularly for ARIMA models, whereas temperature‐weighted averaging provided no additional benefit over simple averaging. Regression models consistently produced the most accurate forecasts of healthy host density. These results show that borrowing information among populations can improve forecasts under specific conditions but is not universally beneficial, highlighting the importance of matching forecasting approaches and data sources to the ecological processes being predicted.
Versions
Related
- Computing the final epidemic size distributions of a multi-type Galton--Watson process
- Epidemic size and host-parasite (co)-evolution: a meta-analysis
- Porcine epidemic diarrhea virus Whole-Genome Diversity Reveals Divergence of Contemporary Populations in the United States
- Distribution Patterns and Intraspecific Spatial Associations of Different Size Classes in Krascheninnikovia ceratoides (L.) Gueldenst. Populations Within the Tibetan Antelope Breeding Grounds of the West Kunlun Mountains
- Object groups mutually bias each other’s perceived average size, as evidenced by neural and behavioural data
BibTeX
@article{paplauskas2026borrowing,
title = {{Borrowing Data From Other Populations to Forecast Epidemic Size}},
author = {Sam Paplauskas},
journal = {Ecology and evolution},
year = {2026},
month = oct,
doi = {10.1002/ece3.74458},
url = {https://europepmc.org/article/PMC/PMC13654656},
note = {CC BY},
}
RIS
TY - JOUR
TI - Borrowing Data From Other Populations to Forecast Epidemic Size
AU - Sam Paplauskas
PY - 2026
DA - 2026/10/01
JO - Ecology and evolution
DO - 10.1002/ece3.74458
UR - https://europepmc.org/article/PMC/PMC13654656
AB - Forecasting infectious disease dynamics is important for ecological management and emerging disease preparedness, yet many systems lack the long‐term datasets required to develop reliable predictions. Spatial replication may provide an alternative by allowing information to be shared among populations, but the extent to which this improves forecasting remains unclear. Here, I used epidemic and temperature data from 20 replicated semi‐natural Daphnia –parasite pond populations monitored across four seasons (80 epidemics) to test whether information from multiple populations improves forecasts and whether environmental similarity identifies informative populations. I forecasted disease prevalence, infected host density, and healthy host density using a benchmark model, autoregressive integrated moving average (ARIMA) and time‐series regression models. Models were trained using focal‐population data alone, mean dynamics across other populations, or temperature‐weighted mean dynamics from environmentally similar populations. Epidemics showed strong seasonal structure, with prevalence and infected host density generally peaking during the warmest period of the season. However, no forecasting approach consistently outperformed others across all variables. Temperature improved forecasts of prevalence and infected host density but not healthy host density. Multi‐population training improved forecasts in some cases, particularly for ARIMA models, whereas temperature‐weighted averaging provided no additional benefit over simple averaging. Regression models consistently produced the most accurate forecasts of healthy host density. These results show that borrowing information among populations can improve forecasts under specific conditions but is not universally beneficial, highlighting the importance of matching forecasting approaches and data sources to the ecological processes being predicted.
SN - 2045-7758
ER -