Innovative prediction models can help reduce the impact of dengue epidemics

People | Human Health

By Isabel Rowbotham, Co-Editor in Chief

Published January 28th, 2022

Dengue fever is an airborne disease that follows climate and seasonal patterns. Prediction models can be useful to forecast dengue epidemics by creating an early warning system to help guide prevention programmes, such as mosquito control and public health campaigns.

Dengue is a viral infection transmitted through the bite of an Aedes aegypti mosquito, most commonly found in tropical and subtropical countries. Dengue is a climate-driven disease and highly sensitive to changes in temperature, rainfall, humidity and wind.

Temperature and rainfall have been shown to be important factors in disease infectiousness and transmission, by affecting the development and lifespan of mosquitoes. In the last decade, average global temperatures have increased by approximately 0.15°C to 0.2°C per decade, impacting dengue epidemiology in endemic countries.

Vietnam is a tropical country with relatively high levels of humidity and rain. Both urban and rural areas have a considerable risk of dengue fever during the summer months. | Katherine McCormack / Unsplash

Humid environments are ideal for mosquito egg survival (over 95% humidity). Moreover, the increase of urbanisation plays an important role in epidemics. Mosquitoes prefer to lay their eggs in water containers inside homes, car tyres and anywhere where rainwater might collect.

However, any variation in the climate can delay dengue incidence by one to two months. Previous studies that have focused on the relationship between climate change and dengue have shown that long-term climate data and comprehensive quantitative modelling are important for epidemic preparedness.

Vietnam’s plan to forecast dengue epidemics

The disease burden of dengue fever is significant. In Vietnam, around two million people get infected yearly. However, these rates are suspected to be underreported, as many may not seek healthcare assistance and in some cases may even be asymptomatic. These factors, combined with changing climate conditions, cause dengue rates to be highly variable.

Therefore, the need for a sophisticated warning system for Vietnam and many other tropical countries would be useful for public health and help build sustainable prevention methods.

‘The disease burden of dengue fever is significant. In Vietnam, around two million people get infected yearly.’

Previous studies have highlighted the usefulness of climate-driven epidemiological surveillance, such as sub-seasonal forecasting, which can predict epidemics between two weeks and two months ahead. In a similar case, Uganda deployed a forecasting programme created in association with the European Centre for Medium-Range Weather Forecasts that used temperature and rainfall to predict Malaria transmissions four months ahead.

Some of the obstacles in predicting epidemics have been the lack of detailed healthcare records and climate data outputs. Researchers from Vietnam believe that using a sophisticated superensemble model, an algorithm that uses statistical regression to measure an association between multiple factors, might be the answer. The spatiotemporal (place and time) probabilistic model combines past healthcare records of outbreaks with Earth observation data to predict future outbreaks at specific locations and times of the year.

Heavy rain in Hanoi, the capital of Vietnam. | Karl JK Hedin / Unsplash

The model was designed in partnership with the World Health Organization (WHO), the United Nations Development Programme (UNDP), the Vietnamese Ministry of Health and other Vietnamese health entities. The objective is to estimate the monthly cases of dengue across the six provinces of Vietnam in real time.

The project collated dengue surveillance data from Vietnamese provinces, historical demographic data and Earth observation data, such as minimum, maximum and average air temperatures. It also included precipitation, daytime humidity, wind speed and sea surface temperature. All from the years 2007 to 2016.

‘This is the first instance of such a model being used in dengue epidemiological data to create an early warning system.’

These models were developed to predict the incidence of dengue up to six months ahead. Whilst results showed that the models were more accurate in predicting one to three months ahead, it was not helpful after the fourth month.

However, the ability to predict outbreaks varied across different regions of Vietnam, as well as throughout different times of the year—the summer months from February to June were more accurate, which also coincided with the peak season of dengue transmission.

Additionally, results showed a difference between North and South Vietnam provinces. The southern provinces experience the majority of cases during peak season compared to northern provinces; with the exception of Hanoi, the capital city, a highly urbanised area with 8,700 dengue cases per year over the past decade.

Researchers believe this may be the first early health warning system to be informed by Earth observations, in order to predict dengue epidemics all year long. These models will give a lead time of one to three months, whereas other models thus far have only provided one or two weeks ahead.

The results showed spatiotemporal variations of the model for each region of Vietnam’s provinces by year and month. It shows the regions in which the model indicated a better performance for predictability (orange), and those with poor performance (blue). This suggests the months February to June were better for predicting dengue outbreaks. | Colón-González / PLoS Med

The benefits of a sophisticated forecasting model

Are superensemble models superior to other competing models? It depends, as individual models can be subject to many biases. The superensemble methodology bridges the gap where other models show forecasting inaccuracies and ultimately may generate more accurate and useful predictions. However, the efficiency of using a superensemble model, in this case, was lost when looking beyond four months ahead.

Moreover, the superensemble modelling has proved useful and reliable in other settings, such as forecasting dengue outbreaks in Puerto Rico and Peru, and influenza outbreaks across the United States.

As the technology improves, we can start understanding more clearly the relationships between disease and climate, as well as the vulnerabilities in society, such as inequality, and how these may exacerbate the impact of disease epidemics. We are now closer than ever to making science-based decisions to prevent or better prepare for an outbreak, as well as preventing the accompanying losses that come with it.

The model was more accurate in detecting dengue outbreaks for a lead of three months ahead, 76% to 68% relative value of forecast (Lead 3). Beyond four months (Lead 4) the relative value drops to 65%. | Colón-González / PLoS Med

Forecasting methods still have limitations

This innovative forecasting model is useful but has various limitations. Some of these include not being able to assess the consequences caused by mosquito control interventions, vector (mosquito) population, serotype-specific circulation, herd immunity and the mobility of people and goods. All these factors are still important in determining the impact of an outbreak.

Moreover, there is still the problem of accounting for underreported cases, the lack of detailed healthcare data at a regional level and confirmed laboratory diagnosis.

‘Researchers believe this is the first early health warning system informed by Earth observations.’

With the current threat of epidemics, it is easy to see the value of investing in better forecasting methods. In the case of the Ebola epidemic in Sierra Leone between 2014 and 2015, real-time forecasting helped make informed decisions, as well as more reliable ones.

This technology has been revolutionary in climate modelling as well as disease modelling. Overall, the study shows promising signs in creating an early warning system for dengue disease outbreaks, which may help guide prevention programmes such as mosquito control and many public health campaigns.

Featured Image: Vince Gx | Unsplash

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