Infectious Disease

Deer Tick, Lyme Disease VectorInfectious diseases like malaria, schistosomiasis, dengue fever, and zika virus are responsible for large burdens of disease globally and are highly sensitive to changes in environmental conditions, including temperature, soil moisture and precipitation patterns, deforestation, dams and irrigation projects, and others. It’s an urgent priority to better understand how land management practices alter the risk of these diseases in different settings and what types of interventions can reduce exposure to these diseases. Most emerging diseases globally are zoonotic diseases (with both human and animal hosts), and clearer understanding of anthropogenic influences on the emergence of zoonotic diseases (like HIV and Ebola) is another priority in planetary health research. Given the implications for food security and livelihoods, as well as for the state of global biodiversity, animal disease is also an important subtheme of disease ecology in the planetary health research context.

Learning Objectives

  • L1: Understand the environment-host-pathogen disease triangle and provide examples.
  • L2: Explain how environmental change can change the incidence, prevalence, geographical distribution, and/or severity of infectious diseases.
  • L3: Describe the criteria for an infectious disease hot spot and explain their characteristics with regard to environmental change.
  • L4: Recognize the interface between human and animal health in the contexts of environmental change and infectious diseases.

 

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Lowe R, Stewart-Ibarra AM, Petrova D, García-Díez M, Borbor-Cordova MJ, Mejía R, Regato M, Rodó X. Climate services for health: predicting the evolution of the 2016 dengue season in Machala, Ecuador. The Lancet Planetary Health [Internet]. 2017;1 (4) :e142-e151. Publisher's VersionAbstract

Background

El Niño and its effect on local meteorological conditions potentially influences interannual variability in dengue transmission in southern coastal Ecuador. El Oro province is a key dengue surveillance site, due to the high burden of dengue, seasonal transmission, co-circulation of all four dengue serotypes, and the recent introduction of chikungunya and Zika. In this study, we used climate forecasts to predict the evolution of the 2016 dengue season in the city of Machala, following one of the strongest El Niño events on record.

Methods

We incorporated precipitation, minimum temperature, and Niño3·4 index forecasts in a Bayesian hierarchical mixed model to predict dengue incidence. The model was initiated on Jan 1, 2016, producing monthly dengue forecasts until November, 2016. We accounted for misreporting of dengue due to the introduction of chikungunya in 2015, by using active surveillance data to correct reported dengue case data from passive surveillance records. We then evaluated the forecast retrospectively with available epidemiological information.

Findings

The predictions correctly forecast an early peak in dengue incidence in March, 2016, with a 90% chance of exceeding the mean dengue incidence for the previous 5 years. Accounting for the proportion of chikungunya cases that had been incorrectly recorded as dengue in 2015 improved the prediction of the magnitude of dengue incidence in 2016.

Interpretation

This dengue prediction framework, which uses seasonal climate and El Niño forecasts, allows a prediction to be made at the start of the year for the entire dengue season. Combining active surveillance data with routine dengue reports improved not only model fit and performance, but also the accuracy of benchmark estimates based on historical seasonal averages. This study advances the state-of-the-art of climate services for the health sector, by showing the potential value of incorporating climate information in the public health decision-making process in Ecuador

Komen K. Could malaria control programmes be timed to coincide with onset of rainfall?. EcoHealth [Internet]. 2017;14 (2) :259-271. Publisher's VersionAbstract

Malaria cases in South Africa’s Northern Province of Limpopo have surpassed known endemic KwaZulu Natal and Mpumalanga Provinces. This paper applies statistical methods: regression analysis and impulse response function to understand the timing of impact and the length that such impacts last. Climate data (rainfall and temperature) are obtained from South African Weather Services (SAWs); global data from the European Centre for Medium-Range Weather Forecasts (ECMWF), while clinical malaria data came from Malaria Control Centre in Tzaneen (Limpopo Province). Data collected span from January 1998 to July 2007. Signs of the coefficients are positive for rainfall and temperature and negative for their exponents. Three out of five independent variables consistently maintain a very high statistical level of significance. The coefficients for climate variables describe an inverted u-shape: parameters for the exponents of rainfall (−0.02, −0.01, −0.02, −0.00) and temperature (−46.61, −47.46, −48.14, −36.04) are both negative. A one standard deviation rise in rainfall (rainfall onset) increases malaria cases, and the effects become sustained for at least 3 months and conclude that onset of rainfall therefore triggers a ‘malaria season’. Malaria control programme and early warning system should be intensified in the first 3 months following the onset of rainfall.

Kibret S, Lautze J, McCartney M, Nhamo L, Wilson GG. Malaria and large dams in sub-Saharan Africa: future impacts in a changing climate. Malaria Journal [Internet]. 2016. Publisher's VersionAbstract

Background

Sub-Saharan Africa (SSA) has embarked on a new era of dam building to improve food security and promote economic development. Nonetheless, the future impacts of dams on malaria transmission are poorly understood and seldom investigated in the context of climate and demographic change.

Methods

The distribution of malaria in the vicinity of 1268 existing dams in SSA was mapped under the Intergovernmental Panel on Climate Change (IPCC) representative concentration pathways (RCP) 2.6 and 8.5. Population projections and malaria incidence estimates were used to compute population at risk of malaria in both RCPs. Assuming no change in socio-economic interventions that may mitigate impacts, the change in malaria stability and malaria burden in the vicinity of the dams was calculated for the two RCPs through to the 2080s. Results were compared against the 2010 baseline. The annual number of malaria cases associated with dams and climate change was determined for each of the RCPs.

Results

The number of dams located in malarious areas is projected to increase in both RCPs. Population growth will add to the risk of transmission. The population at risk of malaria around existing dams and associated reservoirs, is estimated to increase from 15 million in 2010 to 21–23 million in the 2020s, 25–26 million in the 2050s and 28–29 million in the 2080s, depending on RCP. The number of malaria cases associated with dams in malarious areas is expected to increase from 1.1 million in 2010 to 1.2–1.6 million in the 2020s, 2.1–3.0 million in the 2050s and 2.4–3.0 million in the 2080s depending on RCP. The number of cases will always be higher in RCP 8.5 than RCP 2.6.

Conclusion

In the absence of changes in other factors that affect transmission (e.g., socio-economic), the impact of dams on malaria in SSA will be significantly exacerbated by climate change and increases in population. Areas without malaria transmission at present, which will transition to regions of unstable transmission, may be worst affected. Modifying conventional water management frameworks to improve malaria control, holds the potential to mitigate some of this increase and should be more actively implemented.

 

Kibret S, Wilson GG, Ryder D, Tekie H, Petros B. The Influence of Dams on Malaria Transmission in Sub-Saharan Africa. EcoHealth [Internet]. 2015;14 (2) :408-419. Publisher's VersionAbstract

The construction of dams in sub-Saharan Africa is pivotal for food security and alleviating poverty in the region. However, the unintended adverse public health implications of extending the spatial distribution of water infrastructure are poorly documented and may minimize the intended benefits of securing water supplies. This paper reviews existing studies on the influence of dams on the spatial distribution of malaria parasites and vectors in sub-Saharan Africa. Common themes emerging from the literature were that dams intensified malaria transmission in semi-arid and highland areas with unstable malaria transmission but had little or no impact in areas with perennial transmission. Differences in the impacts of dams resulted from the types and characteristics of malaria vectors and their breeding habitats in different settings of sub-Saharan Africa. A higher abundance of a less anthropophilic Anopheles arabiensis than a highly efficient vector A. gambiae explains why dams did not increase malaria in stable areas. In unstable areas where transmission is limited by availability of water bodies for vector breeding, dams generally increase malaria by providing breeding habitats for prominent malaria vector species. Integrated vector control measures that include reservoir management, coupled with conventional malaria control strategies, could optimize a reduction of the risk of malaria transmission around dams in the region.

Nichols E, Gomez A. Dung beetles and fecal helminth transmission: patterns, mechanisms and questions. Parasitology [Internet]. 2013;141 (5) :614-623. Publisher's VersionAbstract
Dung beetles are detrivorous insects that feed on and reproduce in the fecal material of vertebrates. This dependency on vertebrate feces implies frequent contact between dung beetles and parasitic helminths with a fecal component to their life-cycle. Interactions between dung beetles and helminths carry both positive and negative consequences for successful parasite transmission, however to date there has been no systematic review of dung beetle-helminth interactions, their epidemiological importance, or their underlying mechanisms. Here we review the observational evidence of beetle biodiversity–helminth transmission relationships, propose five mechanisms by which dung beetles influence helminth survival and transmission, and highlight areas for future research. Efforts to understand how anthropogenic impacts on biodiversity may influence parasite transmission must include the development of detailed, mechanistic understanding of the multiple interactions between free-living and parasitic species within ecological communities. The dung beetle– helminth system may be a promising future model system with which to understand these complex relationships.
Galvani AP, Bauch CT, Anand M, Singer BH, Levin SA. Human–environment interactions in population and ecosystem health. PNAS [Internet]. 2016;113 (51) :14502–14506. Publisher's VersionAbstract

As the global human population continues to grow, so too does our impact on the environment. The ingenuity with which our species has harnessed natural resources to fulfill our needs is dazzling. Even as we tighten our grip on the environment, however, the escalating extent of anthropogenic actions destabilizes long-standing ecological balances (12). The dangers of mining, refining, and fossil fuel consumption now extend beyond occupational or proximate risks to global climate change (3). Among a plethora of environmental problems, extreme climate events are intensifying (45). Storms, droughts, and floods cause direct destruction, but also have pervasive repercussions on food security, infectious disease transmission, and economic stability that take their toll for many years. For example, within weeks of the catastrophic wind and flood damage from the 2016 Hurricane Matthew in Haiti, there was a dramatic surge in cholera, among other devastating repercussions (67). In a world where 1% of the population possesses 50% of the wealth (8), those worst affected by extreme climatic events and the aftermath are also the least able to rebound.

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