Researchers have devised a method to forecast outbreaks of dengue – a sometimes fatal mosquito-borne disease – as much as four months in advance.
George Sugihara, a mathematical biologist at the Scripps Institution of Oceanography at the University of California San Diego, and mathematician Martin Rypdal at UiT/the Arctic University of Norway, found that the size of human populations susceptible to contracting dengue fever during peak seasons of the year can be related to the stability of cases during off-seasons.
The research is published in the journal Nature Communications.
“This research team has advanced mathematical modeling to predict disease outbreaks months in advance of their occurrence,” says Betsy von Holle, a program officer in NSF’s Division of Environmental Biology, which funded the research.
Adds Sugihara, “Minor dengue infections occurring in the period between outbreaks contain hidden information about how many people will be susceptible to the infection in the next outbreak.
Being able to predict dengue outbreaks this far in advance has immediate public health significance.”
There are an estimated 390 million human cases of dengue fever worldwide every year causing 10,000 to 20,000 deaths.
The research could enable public health officials to take steps such as ordering adequate supplies of medicines or safeguarding vulnerable populations.
Dengue Fever is a common epidemiological mosquito-borne disease in subtropical and tropical regions and has become one of the public health’s biggest challenges.
Dengue is a febrile illness caused by one of the antigenically different serotypes of dengue viruses and mainly transmitted to human through the bite of vectors, including Aedes aegypti and Aedes albopictus [1].
One area that has received particular attention is the association between climatic factors and vector-borne diseases [2].
Among the 193 WHO member countries, more than 70% of the populations are at risk of dengue. WHO reported that 390 million dengue infections occurred every year before 2013 [3].
The dengue infected regions include South-East Asia and Western Pacific, with the most vulnerable area in developing countries. The outbreaks do not only occur in rural areas but also in urban areas.
Taiwan is located in the Pacific Ocean region and is a hotbed of dengue vectors because of its high temperature and humidity [4].
The risk of dengue fever has increased gradually in southern Taiwan and has become a major public health issue that affects the quality of life and the health of Taiwan’s residents.
During the first half of the twentieth century, there were three dengue fever outbreaks in Taiwan (1915, 1931, and 1942). After almost 40 years of dormancy, a dengue fever outbreak reoccurred in 2002 in southern Taiwan.
The total number of indigenous cases in this outbreak was 5336, including 241 cases of dengue hemorrhagic fever (DHF) that caused 19 deaths.
After that, the indigenous dengue cases were less than 400 in 2003–2005. Since 2006, Taiwan has faced dengue fever outbreaks of different scales every year; the cases were concentrated mainly in southern Taiwan, including Kaohsiung City, Tainan City, and Pingtung County. In 2015, Taiwan battled one of the most severe dengue outbreaks in history with over 42,000 dengue cases—22,741 cases in Tainan City, 18,933 cases in Kaohsiung City, and 373 cases in Pingtung City—and 228 deaths found to be associated with dengue infection [5].
Previous studies have been carried out on the correlation between climate factors and dengue fever using a wide spectrum of mathematical and statistical modeling methods [6–10].
Findings from most previous studies in other parts of the world also showed that climatic variables have an effect on dengue fever transmission. Studies in Taiwan [6, 11–13], Singapore [14], Vietnam [8, 15], Thailand [7, 16], China [9, 17], Trinidad [18, 19], Malaysia [20], Puerto Rico [21], Cambodia [10], and Saudi Arabia [22] showed a significant correlation between dengue fever incidence and temperatures, precipitation, and sunshine.
As temperature increases, the Aedes aegypti mosquito displays shorter periods of development in all stages of their life cycle, which leads to increased population growth.
The mosquito feeding rate also increases; and dengue fever viruses in adult Aedes aegypti mosquitoes require shorter incubation periods to migrate to salivary glands [6–10, 14, 23].
Specifically, increasing temperatures increases the available habitat for the dengue fever vector, the Aedes aegyptimosquito, while concurrently increasing both the longevity of the virus and the mosquito [14].
Higher temperatures can also shorten the duration of virus replication, and increase mosquito reproduction and contacts with humans [9].
If temperature increases by approximately 3 °C, mean incidence rates during epidemics can double [24].
Warmer temperatures can increase the transmission rates of dengue fever in various ways. It may allow vectors to survive and reach maturity much faster than at lower temperatures [25].
Moreover, it may also reduce the size of mosquito larvae resulting in smaller adults that have high metabolism rates, requiring more frequent blood meal and need to lay eggs more often [25].
Some studies reported that rainfall can lead to increases in dengue fever transmission. They suggested that rainfall creates abundant outdoor breeding sources for Aedes aegypti, and the water storage containers also can serve as breeding habitats.
Bhatt, Gething, Brady, Messina, Farlow, Moyes, Drake, Brownstein, Hoen, Sankoh, Myers, George, Jaenisch, Wint, Simmons, Scott, Farrar, and Hay paired the resulting risk map with detailed longitudinal information from dengue fever cohort studies, and they predicted dengue fever to be ubiquitous throughout the tropics, with local spatial variations influenced strongly by rainfall [23].
Choi, Tang, McIver, Hashizume, Chan, Abeyasinghe, Iddings, and Huy developed negative binomial models using monthly average maximum, minimum, mean temperatures and monthly cumulative rainfall, and they also claimed that rainfall significantly increased the dengue fever incidence [10].
When more consecutive wet days occurred in a period, dengue fever incidence increased. Rainfall leads to an increase in breeding sites of the mosquito vector, which would contribute to the increase in dengue fever occurrence [21].
On the contrary, however, some other studies showed that heavy rainfall can possibly lower dengue fever transmission by reducing the survival rate of the Aedes aegypti mosquito. Wegbreit analyzed weekly dengue fever morbidity data from the twin-island country of Trinidad and Tobago, and he suggested that there is a slightly negative correlation with the precipitation [26].
Thammapalo, Chongsuwiwatwong, McNeil, and Geater determine the independent effects of rainfall [16] in Thailand, and they also found that increased rainfall is associated with a decreased incidence of dengue fever cases in some provinces. Alshehri [22] aimed to address the effects of heavy rainfall on Aedes aegypti mosquito density in Saudi Arabia, and he argued that dengue fever has negative correlation with rainfall and humidity.
Sunshine is also closely linked to other ecological factors such as temperature and humidity and thereby might affect the dengue fever incidence [15].
Correlation studies carried out on monthly dengue fever cases have found the risk of dengue fever to be inversely associated with duration of sunshine [8].
With the monthly data in Vietnam, Vu, Okumura, Hashizume, Tran, and Yamamoto indicated that there is a significant negative association between dengue fever cases and the hours of sunshine [15].
Wongkoon, Jaroensutasinee, Jaroensutasinee investigated the effect of seasonal variation on the abundance of Aedes aegypti mosquito larvae and explored the impact of weather variability on dengue fever transmission in Thailand, and they concluded that maximum temperature, sunshine and evaporation are negatively correlated with dengue fever incidence [19].
However, while most studies claimed that climate is a determinant of dengue fever, some other studies argued that climate factor has no obvious correlation with this disease.
They suggested that temperature [18, 27–29] and rainfall [13, 27–29] did not affect dengue fever incidence.
The weekly average maximum temperature, total rainfall and the total number of dengue fever cases from 2005 to 2011 were used as time series data in Goto, Kumarendran, Mettananda, Gunasekara, Fujii, and Kaneko’s study [27].
They found that weekly average maximum temperatures and the weekly total rainfall did not significantly affect dengue fever incidence in three geographically different areas of Sri Lanka.
Pandey, Nagar, Gupta, Khan, Singh, Mishra, Prakash, Singh, Singh, and Jain reported the annual trend of dengue fever virus infection in north India [28], and they indicated that there is no statistical significant correlation between weather data and increasing dengue fever positive cases.
In a population-based study on the effects of climate and mosquito indices on dengue fever in Trinidad, Chadee, Shivnauth, Rawlins, and Chen declared that no significant correlations are observed between temperature and dengue fever [18].
Chang, Lee, Ko, Tsai, Lin, Chen, Lu, and Chen pointed out that climatic factors correlated significantly with case numbers of many diseases, such as murine typhus and Q fever, but neither temperature nor rainfall correlated with the case number of dengue fever [13].
According to the epidemiological investigation, the incidence of dengue fever had no relationship with temperature, or precipitation, and some studies [29] showed a clear relationship only with the sociological factors.
Most of the climatic data are range-type data. Because of the limitation of traditional statistics (e.g., regression analysis), range-type data is difficult to be analyzed. Most of them are analyzed by minimum value (e.g., minimum temperature), maximum value (e.g., maximum temperature), mean value (e.g., mean temperature, mean rainfall), and cumulative value (e.g., cumulative rainfall, cumulative sunshine).
Most studies developed linear regression model [6–10] or negative binomial regression model [10] using monthly average temperatures [14, 17], maximum temperatures [7, 27], minimum temperatures [10], mean rainfall [14, 17], cumulative rainfall [27], and cumulative sunshine [8, 15, 19] over the period for the relationship between dengue fever incidence and climatic data.
However, the major drawback of the traditional statistical methods is that when the correlation between dengue fever and each of the above-mentioned value is not consistent, it will be difficult to draw a conclusion [10, 19].
For example, in Choi, Tang, McIver, Hashizume, Chan, Abeyasinghe, Iddings, and Huy’s study [10], mean temperature is significantly associated with dengue fever incidence, but dengue fever incidence did not correlate well with the maximum temperature and minimum temperature.
Wongkoon, Jaroensutasinee, Jaroensutasinee’s study also had the same problem.
They investigated the effect of seasonal variation on the abundance of the Aedes aegypti mosquito larvae and explored the impact of weather variability on dengue fever transmission in Thailand, and they found that mean temperature and minimum temperatures are positively associated with dengue fever incidence, but maximum temperature is negatively correlated with dengue fever incidence [19].
With the advent of information technology, very large datasets have become routine. Traditional statistical methods do not have the power or flexibility to analyze these efficiently and extract the required knowledge.
Symbolic data analysis is to summarize a large dataset in such a way that the resulting summary dataset is of a manageable size and yet retains as much of the knowledge in the original dataset as possible [30, 31].
One consequence of this is that the data may no longer be formatted as single values, but be represented by lists, intervals, distributions, etc. The summarized data have their own internal structure, which must be taken into account in any analysis.
High peaks for dengue outbreak is reported on summer in Taiwan.
This suggests that climatic factors are likely to exert potential impact on dengue fever outbreak in tropical or subtropical regions [32, 33].
This study is aimed for investigate the relationship between climatic factors and the outbreaks of dengue fever in southern Taiwan with symbolic data analysis and to compare the differential effects of climatic factors on the incidence of dengue fever in southern Taiwan.
More information: Martin Rypdal et al. Inter-outbreak stability reflects the size of the susceptible pool and forecasts magnitudes of seasonal epidemics, Nature Communications (2019). DOI: 10.1038/s41467-019-10099-y
Journal information: Nature Communications
Provided by National Science Foundation
