Battle against Dengue fever: Scientists claim they can now predict the next outbreak up to 4 months in advance

There is currently no definitive treatment, so prevention is critical in reducing the number of people affected. Hence, if scientists are able to predict an epidemic, healthcare providers would be better positioned to prevent and control it


                            Battle against Dengue fever: Scientists claim they can now predict the next outbreak up to 4 months in advance

Predicting the next outbreak of a dangerous viral disease like dengue — which has increased 30-fold over the last 50 years, according to the World Health Organization (WHO) — has remained highly challenging for experts until now.

Scientists have now addressed this complex problem by developing dengue forecasting models for individual seasons, which can predict where the next outbreak is expected to be, as far as up to four months in advance. The team says this will facilitate the optimal distribution of vaccines in space and time. 

''The projections mentioned — total cases, peak week, and the number of cases every week — can be forecasted very well by some models up to four months in advance," biocomplexity scientist Dr. Matteo Convertino from Hokkaido University tells MEA WorldWide (MEAWW). 

"These projections (that are about the disease) are important for distributing the number of vaccines efficiently to health centers for instance. Additionally, for policymakers, we can provide all potential trajectories of how dengue virus will spread because many (ideally all) scenarios are considered," he adds.

"For instance, climate change and other scenarios can be considered. Thus, this can guide disease-focused ecosystem development," says Dr. Convertino, who is an associate professor and principal investigator of the Nexus Group at the Graduate School of Information Science and Technology, Hokkaido University.
  
He explains that each individual model can provide a fine-scale indication of what factors are important in the spread, such as environmental factors (for example, runoff) and human factors (mobility).

"This is useful for epidemiologists that are interested in how the disease spreads and yet also for healthcare people. Healthcare can be directed to high-risk areas where certain transmission factors are important," says Dr. Convertino.

Dengue is a mosquito-borne viral infection most commonly found in the Caribbean, the Americas, and South-East Asia. It causes severe flu-like illness and sometimes causes a potentially lethal complication called severe dengue, which could be life-threatening.

According to the WHO, the global incidence of dengue has grown dramatically in recent decades, and about half of the world's population is now at risk. There are an estimated 390 million infections each year.

Severe dengue is a leading cause of serious illness and death among children and adults in some Asian and Latin American countries. "One model estimate indicates 390 million dengue infections per year, of which 96 million manifest clinically (with any severity of disease)."

"Another study on the prevalence of dengue estimates that 3.9 billion people are at risk of infection with dengue viruses. Despite a risk of infection existing in 128 countries, 70% of the actual burden is shouldered by Asia," says the WHO.

Dengue is a mosquito-borne viral infection most commonly found in the Caribbean, the Americas, and South-East Asia. There is no specific treatment for dengue/severe dengue currently.
(Getty Images)

Major dengue epidemics occur every two to five years, depending on climate fluctuations. There is currently no definitive treatment, so prevention is critical in reducing the number of people affected. If scientists are able to predict an epidemic, healthcare providers would be better positioned to prevent and control it.

"Infectious diseases pose a continuing and dynamic threat globally. The mosquito-transmitted dengue viruses, for example, are endemic throughout the tropical regions of the world and infect millions of people each year," says the study published in the Proceedings of the National Academy of Sciences (PNAS).

"In endemic areas, dengue incidence has a strong seasonal pattern but also exhibits strong interannual variation with major epidemics occurring every 2-5 years. As for many other infectious diseases, timely largescale effective interventions are needed to reduce the serious impacts of dengue epidemics on health, healthcare systems, and economies. Unfortunately, these epidemics have proven difficult to predict, hindering efforts to prevent and control their impact," the study adds.

To address these challenges, multiple US government agencies within the Pandemic Prediction and Forecasting Science and Technology Working Group initiated the Dengue Forecasting Project in 2015. 

A total of 16 teams from around the world were given the same dengue incidence and climate data for Iquitos, Peru, and San Juan, Puerto Rico, to understand the universal drivers of Dengue epidemics. Dr. Convertino led one of the 16 teams in the project. 

The researchers combined disease and climate data from previous years and the current season into complex models and tried to predict how the season would progress before the occurrence of outbreaks.

Two dengue-endemic cities were identified as they had long-term incidence and climate data. These locations were also selected due to their proximity to the US.

The researchers worked with epidemiologists from endemic regions to identify three crucial forecasting targets: how intense will the peak of the epidemic be (maximum weekly number of cases), when will that peak occur (the week when maximum cases occur) and the total number of cases in a season.

"Accurate forecasts of these outcomes could improve the allocation of resources for primary prevention (for example, risk communication, vector control) or secondary prevention (for example, planning medical staffing, preparing triage units)," says the study.

Fogging done to eliminate mosquito for preventing the spread of dengue fever. (Getty Images)

The teams used a variety of modeling approaches, and data sources provided forecasts for each of the three targets for both locations at 13 seasonal time points over four seasons. Submitted forecasts varied widely.

For example, forecasts with data up to week 12 of the 2012-2013 season predicted that the epidemic in San Juan might be one of the smallest or largest on record.

Forecasts improved substantially as the season progressed and data accumulated. Even in the early weeks, some of the models provided reliable information about what was likely to happen, says the study.

"By the end of the season, most forecasts accurately identified the peak incidence and peak week. Independent of the forecast model, accuracy varied by location, target, and season. Though peak incidence and total incidence forecasts were not comparable, in general, peak week forecasts were more accurate for Iquitos than for San Juan," the findings state.

"The lower accuracy for seasons with higher peaks confirms one of the core challenges of forecasting dengue epidemics. Seasonal cycles are generally predictable, but the abnormal epidemic seasons, which are the biggest challenge for public health, are the hardest season to predict," the study adds.

The team says the results highlight the difficulties in predicting dengue epidemics, even in endemic areas with clear seasonal transmission patterns. However, the results also emphasize the need to reconstruct multiple models to more fully represent all potential epidemic trajectories, as when predicting climate changes, say experts.

According to experts, while the challenges are formidable, through forecasting projects like this one, the community can move this research forward, translating the research into public health tools that can transform the way countries prepare for and respond to epidemics.

"Some models (described in the study) perform better for short term forecasts versus long term ones. But this is the beauty and power of putting together multiple models: they provide all useful answers at different scales (space and time) in a way that the collective forecast is much more useful than the individual one," Dr. Convertino says.

"Thus, it is not about what model is the best (this is a rather outdated question) because any model represents just one line of evidence/reality," he adds.

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