SARIMA Forecasts of Dengue Incidence in Brazil, Mexico, Singapore, Sri Lanka, and Thailand: Model Performance and the Significance of Reporting Delays

Timely and accurate knowledge of Dengue incidence is of value to public health professionals because it helps to enable the precise communication of risk, improved allocation of resources to potential interventions, and improved planning for the provision of clinical care of severe cases. Therefore, many national public health organizations make local Dengue incidence data publicly available for individuals and organizations to use to manage current risk. The availability of these data has also resulted in active research into the forecasting of Dengue incidence as a way to increase the public health value of incidence data. Here, we robustly assess time-series-based forecasting approaches against a null model (historical average incidence) for the forecasting of incidence up to four months ahead. We used publicly available data from multiple countries: Brazil, Mexico, Singapore, Sri Lanka, and Thailand; and found that our time series methods are more accurate than the null model across all populations, especially for 1-and 2-month ahead forecasts. We tested whether the inclusion of climatic data improved forecast accuracy and found only modest, if any improvements. We also tested whether national timeseries forecasts are more accurate if made from aggregate sub-national forecasts, and found mixed results. We used our forecasting results to illustrate the high value of increased reporting speed. This framework and test data are available as an R package. The non-mechanistic approaches described here motivates further research into the use of disease-dynamic models to increase the accuracy of medium-term Dengue forecasting across multiple populations. Author summaryDengue is a mosquito-borne disease caused by the Dengue virus. Since the Second World War it has evolved into a global problem, securing a foothold in more than 100 countries. Each year, hundreds of millions of people become infected, and upwards of 10,000 die from the disease. Thus, being able to accurately forecast the number of cases likely to emerge in particular locations is vital for public health professionals to be able to develop appropriate plans. In this study, we have refined a technique that allows us to forecast the number of cases of Dengue in a particular location, up to four months in advance. We test the approach using state-level and national-level data from Brazil, Mexico, Singapore, Sri Lanka, and Thailand. We found that the model can generally make useful forecasts, particularly on a two-month horizon. We tested whether information about climatic conditions improved the forecast, and found only modest improvements to the forecast. Our results highlight the need for both timely and accurate reports. We also anticipate that this approach may be more generally useful to the scientific community; thus, we are releasing a framework, which will allow interested parties to replicate our work, as well as apply it to other sources of Dengue data, as well as other infectious diseases in general.