Dengue is the fastest-growing mosquito-borne viral infection in the world today. It is present in over 150 countries, meaning that around 40 percent of the world’s population now live in countries where dengue is a daily risk. It has been estimated that annually dengue affects 390 million people and has a global cost of almost US$9 billion per year.

The Dengue MOdel forecasting Satellite-based System (D-MOSS) is the first fully integrated dengue fever forecasting system incorporating Earth Observation (EO) data and seasonal climate forecasts to issue warnings on a routine basis.

The system is being piloted in Vietnam since June 2019. The country had suffered major dengue outbreaks in preceding years, seeing cases increase from an annual average of 35,000 (in 2000-2002) to 206,000 (in 2017-19). As in many countries, the existing hydro-meteorological and environmental ground-based monitoring networks were sparse, and unable to provide enough data to be useful in forecasting dengue outbreaks. This is where satellite data is so valuable; it can provide an accurate and consistent representation of the spatial variation of key environmental, and socio-economic parameters.

D-MOSS integrates data derived from a variety of different sources: live and historical satellite data, on-the-ground observations, climate and meteorological data and other geo-located information known to be related to dengue outbreaks. The biology of mosquitoes is influenced by local weather conditions such as temperature, humidity and wind speed, all of which can influence how often they bite, how long they survive, how far they spread, and how many eggs they produce. D-MOSS also includes a surface water availability component as this is can be a key factor in mosquito breeding habits. Once the data is collected it is fed into statistical forecasting models of disease incidence, which produce the forecasts of dengue outbreaks. These forecasts are made available on a simple, regularly updated user interface, which shows the likelihood and location of an outbreak up to six months in advance.

The principle is a simple one: predict when and where dengue outbreaks will occur, well before mosquitoes start to breed, and people start to get sick. This allows preventative measures to be taken. Authorities on the ground can strategically plan interventions in plenty of time, moving precious resources to where they are most effective. These may range from media awareness campaigns, spraying insecticides, and the removal of standing water, through to warning communities to cover up their skin and sleep under mosquito nets.
D-MOSS takes the form of a web-based platform. The interface has been designed to be user-friendly, after a series of consultations with end users. The system’s architecture is based on open and non-proprietary software, where possible, and on flexible deployment into platforms including cloud-based virtual storage and application processing.

It was recognised that strong stakeholder engagement and co-development of D-MOSS in partnership with the end users would be critical to the success of the project. Stakeholders were engaged with via formal and informal meetings, workshops, regular telecons, surveys and questionnaires, as well as innovative methods such as games. Working in partnership with key Vietnamese stakeholders has allowed them to define the functionality and requirements of the system and to ensure that predictions are produced at lead times that are specifically aligned with decision-making processes in Vietnam.

Early results from Vietnam indicate that D-MOSS is able to change Vietnam’s reactive approach to dengue prevention to a more proactive one. The system is shown to be reliable, cost-effective, and can be easily replicated elsewhere, at a range of different scales. It has enabled us to develop new post-processing techniques for EO data products, as well as has underlined the value of strong stakeholder engagement. D-MOSS systems are currently being piloted in Malaysia and Sri Lanka, and advanced discussions are also taking place with other countries in South-East Asia.

The D-MOSS project is funded by the UK Space Agency’s International Partnership Programme.

Introduction: Influenza, commonly known as flu, is an infectious viral disease that causes an acute infection of the respiratory tract. The disease can easily spreads from person to person. It is responsible for millions of cases of severe illness and up to 650.000 deaths per year worldwide (CDC, 2018; WHO, 2018). In Germany, there are about 4 million influenza cases per year, but only a small fraction is reported. Influenza, on average, causes about 7 thousand deaths in Germany each year.
In light of the COVID-19 pandemic the interactions and mechanisms between virus spread, infection rate, environmental conditions, incidence and the severity of health effects, caught new attention (e.g. Sullivan et al., 2020). Contrary to the Corona virus, longer and thus statistically more reliable time series of influenza cases can be examined that might strengthen the research and understanding of COVID-19 transmission and its interaction with environmental conditions. While there are several studies that examine the relationship between temperature, humidity and influenza (e.g. Zhang et al., 2015, Jaakkola et al., 2014), there are only very few that propose effects by PM2.5 (DeFelice, 2020). However, all these studies are carried out under moderate to high air pollution conditions (e.g. Wong et al., 2009), for selected cities (e.g. Huang et al., 2016; DeFelice, 2020) or on aggregated national levels (e.g. Thompson et al., 2004).
Methods: In this study we combine air pollution data from the Copernicus Atmosphere Monitoring Service (European air quality ensemble re-analysis) (Marecal et al., 2015, Meteo France, 2021), meteorological data from the ECMWF ERA-5 (Sabater, 2019) and health data from the AOK-BW health insurance company (AOK-BW, 2021) to examine the relationship between influenza and the environmental stressors air temperature, PM2.5, PM10, O3 and NO2 in Baden-Württemberg, Germany from 2010 to 2018 at zip code level. For this purpose, AOK-BW has shared a unique data base that contains information about influenza incidence and indicators of severity of the disease from over 4 million people over an 8-year observation period. AOK-BW has approximately 4.2 million clients living spread out within the federal state of Baden-Württemberg, and thus a coverage of ca. 38% of the population in that state. The presented analysis at high spatial resolution enables to delineate differences among regions, within cities or between urban and rural areas. Baden-Württemberg is characterized by low to moderate pollution levels and a temperate climate. For statistical analysis, a generalized additive model was applied to analyze the association between influenza incidence, quarterly means of PM 2.5, PM10, O3, NO2 and air temperature with smoothing functions based on P-Splines.
Results: Our findings suggest a positive relationship between PM2.5 and influenza risk after adjusting for temperature and seasonal trends. The estimated effect of PM2.5 increased with higher PM2.5 values stabilizing at about 15 µg/m³ (seasonal average). For mean temperature, a negative relationship was observed; lower mean temperature values were associated with a higher risk for an infection by the influenza virus.
Discussion: The results demonstrate strong influences by environmental factors on the incidence of influenza. Even though the observational study design does not allow inference of causality, the results can guide discussion of potential mechanisms. Both temperature and PM2.5 demonstrated significant and substantial effects in our results, with temperature effects being somewhat predominant. This is in keeping with previous strands of research, proposing destabilization of the influenza virus at warmer temperatures, stabilization of the virus against ultraviolet light by particulate matter, but also with sensitization of respiratory epithelia with exposure to cold air and particulate matter. Results can also be used to improve the management and forecasting of influenza epidemics and to reduce the mortality associated with it, which is crucial for health service providers like AOK-BW. The findings may also contribute to a better understanding of virus transmission and the severity of the disease depending on the abundance and variability of environmental stressors.

The Ebola virus is the causing agent of Ebola virus disease (EVD), an emerging infectious disease that can reach a 90% mortality rate in infected populations. The largest epidemic took place in West Africa in 2014-2016, whereas previously outbreaks had been confined to Central Africa. Multiple ecological and social factors may interact to cause an Ebola virus outbreak. Increased human – wildlife contacts in degraded forests or modified landscapes may lead to a higher risk of outbreaks.
In this study we combine ecological knowledge and Earth observation to model Ebola virus transmission in a bat species (Hypsignathus monstrosus) suspected to be a reservoir for the virus. Land use maps, derived from satellite images, and information on habitat use by bats were integrated to a spatially explicit model to study how land use changes may influence virus transmission in this species. We used land use maps from 2005 and 2015 of an area of approximately 190km2 in Guinea, where bat-human interactions are common, as a starting point for our simulations. During this period in the study area, forest area decreased while the surface of forest-agricultural mosaics and savannahs increased, reflecting a common landscape transformation in the region. Habitat use by bats was modeled based on previous and on on-going ecological studies on the species. Virus transmission among bats is more likely when groups of individuals come together, such as during roosting and foraging. To integrate this information into our model, we assigned indexes of habitat suitability to each land use for these two behaviors.
Results of our simulations show how land use changes affect animal behavior of an Ebola virus host, and the impact this virus can have on transmission within host populations. Deforestation is considered an important risk factor in the emergence of some infectious diseases, notably Ebola. Western Africa has undergone important landscape and human population changes in the last few decades. This study shows a novel way to integrate Earth observation into health ecology research to study the emergence of infectious diseases, with the possibility to test in silico hypotheses on the role of animal reservoirs.

It has been recognized (Sathyendranath et al. 2020) that networking and collaboration across scientific disciplines and with the stakeholder communities are essential components of capacity building, to ensure that the benefits of scientific advances reach the communities at risk, and on a global scale. To promote networking among scientists working on various aspects of water-associated diseases, a two-year network project called ONWARD (Open network for Water Related Diseases) has been initiated, with funding from UK Research and Innovation GCRF (Grand Challenges Research Fund). The project recognizes that forecasting outbreaks of water-associated diseases and their geo-referenced risk mapping is a complex matter for which the collaboration of experts from several disciplines (ranging from environmental biochemistry, genetics, molecular biology, and epidemiology to remote sensing and modelling) is needed, if we are to make real advances. Until now, the required disciplines have not belonged to the same community, and their scientists have rarely encountered each other in a scientific setting. A multidisciplinary network is essential to foster exchange of ideas between the required experts, and so build a collaborative approach to a difficult problem, and the ONWARD network has been built towards this goal.
The restrictions on travel and other constraints imposed by the COVID-19 pandemic necessitated significant changes to the initial plans envisaged within ONWARD, with most of the activities going online, instead of being in person. The network has been organizing a series of webinars on various aspects of water-borne and vector-borne diseases in relation to environmental and sanitation conditions, in collaboration with the AIR Centre of Portugal, with international invited speakers. A training course is being organized in India with network members as teaching faculty, and this will be followed by another one in Brazil. ONWARD is also contributing to a special session at the Living Planet Symposium 2022 that deal with Earth Observation and Health.
One of the challenges with networking projects is how to maintain the momentum built through the project, when the project itself is completed. To this end, we are exploring the possibility of engaging with charitable foundations, notably the Trevor Platt Science Foundation, set up in memory of Prof. Trevor Platt, one of the moving spirits behind the ONWARD project.

References:
Sathyendranath, S, Abdulaziz, A, Menon, N, George, G, Evers-King, H, Kulk, G, Colwell, R, Jutla, A, Platt, T (2020) Building capacity and resilience against diseases transmitted via water under climate perturbations and extreme weather stress. In “Space Capacity Building in the XXI Century”, Ferretti, S (Ed.), Publisher: Springer Nature Switzerland AG.

Sustainable Development Goal (SDG) to “ensure healthy lives and promote well-being for all at all ages” (Goal 3) has the laudable target: “By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseases” (SDG Target 3.3). The SDG indicator 3.d aims to “Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risks”. Natural disasters such as floods have the potential to destroy lives, displace populations, breakdown infrastructure, destroy sanitation facilities and impede access to food, clean water and medicine. Furthermore, flooding often leads to outbreaks of water-borne diseases such as Cholera. Therefore, strategies for attaining SDG Target 3.3 must include improved responses to flooding, and a first step in the response must be to identify flooded areas. The mapping must cover the entirety of affected areas and has to be rapid, to be effective. In this study, we present the application of remote sensing to map flooded areas, using the Vembanad-Kol-Wetland System in the southwest of India as a case study. The Vembanad-Kol-Wetland System is an area of outstanding natural beauty that is protected by both national and international treaties and forms an important resource for local communities. In August 2018, the southwestern India experienced an extremely heavy monsoon season, which caused major flooding in the Vembanad Lake region. Although flooding during the monsoon season is not uncommon in the region, the floods of August 2018 were described as a once-in-a-hundred-year event with nearly 500 deaths and over a million people losing their homes. Under these circumstances, accurate flood mapping is an essential service to support decision making by local authorities on selecting the most vulnerable areas for targeted rescue missions and for prioritising health care to avoid outbreaks of diseases. Synthetic Aperture Radar (SAR) on board Sentinel-1 has the capability to provide such a service. However, the temporal coverage of Sentinel-1 is relatively low (10-20 days) and it is important to consider the use of other satellite sensors to fill the gaps in between consecutive Sentinel-1 overpasses. Here we reviewed the use of existing algorithms to map water and land using the spectral reflectances of the green, red and near-infrared bands from the Multi Spectral Imager (MSI) sensor on board Sentinel-2 to map the flooded areas in Lake Vembanad during August 2018. Although spectral radiometers that operate in the visible and infra-red domains have no cloud-penetrating capability (unlike SAR sensors), we show that remote sensing by MSI can be used to generate flood maps to compliment SAR-based techniques to enhance temporal coverage (~5 days). In addition, multi-spectral visible remote sensing may be used for analysis of water quality as well as risk mapping of environmental Vibrio cholerae incidence, providing additional important services during natural disasters.

In 2017, diarrheal diseases were responsible for 1.57 million deaths, of which 534 000 concerned children under 5-years (GBD, 2017; Troeger et al. 2020). This situation is due to domestic and recreational use of polluted surface waters, deficits in hygiene, access to healthcare and drinking water, and to weak environmental and health monitoring infrastructures. The highest health burden, 606 024 deaths, occurs in Sub-Saharan Africa where only 56.8% of the population has currently access to piped water, mainly in urban center (Reiner et al. 2018). In this region, in 2017, 85 million people, the large majority of whom live in rural areas, were still using unfiltered surface water as their drinking and domestic water source and 29% of the rural population practice open defecation (JMP, 2019). Moreover, climate change is expected to impact water resources both in quantity and quality and to potentially boost the presence, dissemination and transmission of pathogens (Carlton et al. 2014, 2016). The resulting impact of climate change is expected to increase the relative risk of diarrhea illness in the tropics and subtropics: 8% to 11% by 2010-2039 and 22% to 29% by 2070-2099 (Kolstad & Johansson, 2011). Furthermore, the extension of cultivated areas promotes surface runoff that increases suspended particulate matter (SPM, Robert et al. 2016, 2017) and possibly microbial pathogens occurrences in surface waters (Rochelle-Newall et al. 2015; Cecchi et al. 2020). Finally, Sub-Saharan Africa is undergoing major changes in terms of demographic growth especially in rural areas (Mercandalli & Losch, 2018) and political insecurity. All of these factors challenge access to clean water and healthcare and highlight the need to monitor water quality at the large scale.

In the study area located in Burkina Faso, diarrheal diseases represent the 3rd cause of consultation, with children under 5 years old the most impacted.
The most common diarrheal diseases are caused by the presence of microorganisms (bacteria, viruses) in surface water. Escherichia coli (E. coli) is an indicator for the enteric pathogens that cause many diarrheal diseases. The links between E. coli, diarrheal diseases and environmental parameters have not received much attention in West Africa. This case study, carried out Bagre Reservoir, aims at filling this knowledge gap by analyzing the environmental variables that play a role in the dynamics of E. coli, and cases of diarrhea (Robert et al. 2021).
A particular focus is given to satellite-derived parameters to assess whether remote sensing can provide a useful tool to assess health hazard: rainfall (GPM IMERGHHV6 data product), water level (Altimetry data from Jason-3), surface water reflectance and area, as well as NDVI (Sentinel 2), Land Surface Temperature (MODIS LST 8-day composite data for daily and nightly overpasses). Indeed, tele-epidemiology appears as a powerful tool to study climate-environment-health relationship and to both understand and predict the spatio-temporal distribution of pathogens through the use of satellite data. Thus, the implementation of a tele-epidemiological project integrating environmental, microbiological and epidemiological variables will represent a major asset in regions where in situ measurements are scarce and where the impacts of climate change are most strongly felt (Niang et al. 2014).
Samples of surface water were routinely collected to measure E. coli, enterococci and suspended particulate matter (SPM) at a monitoring point (Kapore) during one year. In addition, satellite data were used to estimate precipitation, water level, Normalized Difference Vegetation Index (NDVI) and SPM. Monthly epidemiological data for cases of diarrhea from three health centers were also collected and compared with microbiological and environmental data.
A positive correlation between E. coli and enterococci in surface waters was found indicating that E. coli is an acceptable indicator of fecal contamination in this region.
E. coli and diarrheal diseases were strongly correlated with monsoonal precipitation, in situ SPM, and Near Infra-Red (NIR) band between March and November. Partial least squares regression showed that E. coli concentration was strongly associated with precipitation, Sentinel-2 reflectance in the NIR and SPM, and that the cases of diarrhea were strongly associated with precipitation, NIR, E. coli, SPM, and to a lesser extent with NDVI. Therefore intervention though public education/awareness should be increased as the rainy season approaches.
Moreover, E. coli dynamics were reproduced using satellite data alone, particularly from February to mid-December (R² = 0.60) as were cases of diarrhea throughout the year (R² = 0.76). This implies that tele-epidemiology approach through the use of satellite data could provide an important contribution to water quality monitoring, and thus contribute to the establishment of warning systems.

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Carlton E.J, Woster A.P, DeWitt P, Goldstein R.S, Levy K. A Systematic Review and Meta-Analysis of Ambient Temperature and Diarrhoeal Diseases. International Journal of Epidemiology. 2016; 45, 1: 117–30. https://doi.org/10.1093/ije/dyv296 PMID: 26567313
Cecchi P, Forkuor G, Cofie O, Lalanne F, Poussin J-C, Jamin J-Y. Small Reservoirs, Landscape Changes and Water Quality in Sub-Saharan West Africa. Water. 2020; 12, 7: 1967. https://doi.org/10. 3390/w12071967. PMID: 33274073
Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2017 (GBD 2017) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018.
JMP. Progress on household drinking water, sanitation and hygiene 2000–2017. Special focus on inequalities. New York: United Nations Children’s Fund (UNICEF) and World Health Organization (WHO). 2019. Available from: https://www.who.int/water_sanitation_health/publications/jmp-report-2019/en/
Kolstad E.W, Johansson K.A. Uncertainties Associated with Quantifying Climate Change Impacts on Human Health: A Case Study for Diarrhea. Environmental Health Perspectives. 2011; 119, 3: 299–305. https://doi.org/10.1289/ehp.1002060 PMID: 20929684
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