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Theoretical epidemic curves representing no surveillance (pink curve) vs passive surveillance (red curve) and response (top panel) and integrating an early warning system (blue curve, middle panel), showing the timeliness of surveillance, detection, and response. When only using passive surveillance (top panel), the first cases are detected only after enough transmission results in individuals showing symptoms, seeking medical attention, and getting tested for the pathogen, and whose doctor reports positive results. Additional time is then required to plan, organize, and initialize a response. In the middle panel, forecasts of high-risk conditions trigger an early warning alert that will initiate screening. Through screening or active surveillance, an infected individual can be detected much earlier and therefore the response also can be initialized earlier, reducing the magnitude of the outbreak. During the screening or active surveillance phase, it may also be possible to prepare for a response in the event that a case is detected and therefore reduce the time between detection and response. The steps and connections within an early warning system are shown on the bottom
Source publication
Purpose of Review
Weather and climate influence multiple aspects of infectious disease ecology. Creating and applying early warning systems based on temperature, precipitation, and other environmental data can identify where and when outbreaks of climate-sensitive infectious diseases could occur and can be used by decision makers to allocate resour...
Citations
... 44 Investment in preventive public health measures, such as climate-based early warning and infectious diseases forecasting, allows early identification of conditions that may be conducive to a disease outbreak: this can enable effective public health interventions to be deployed in time to mitigate morbidity and mortality if the outbreak occurs. 45 Mitigation of the impacts of climate change on infectious diseases transmission patterns requires collaboration across various disciplines to operationalize pertinent practices across the spectrum of public health principles. 11 Based on our analysis, we argue that medical hegemony, or the dominance of curative medicine in health systems of Nepal, is entrenched in Nepalese society, which prefers medical graduates-intrinsically trained in individualoriented medical care-to be the practitioners of public health. ...
Background
To explore the impacts of contextual issues encompassing social, cultural, political and institutional elements, on the operation of public health surveillance systems in Nepal concerning the monitoring of infectious diseases in the face of a changing climate.
Methods
Semi-structured interviews (n = 16) were conducted amongst key informants from the Department of Health Services, Health Information Management System, Department of Hydrology and Meteorology, World Health Organization, and experts working on infectious disease and climate change in Nepal, and data were analysed using thematic analysis technique.
Results
Analysis explicates how climate change is constructed as a contingent risk for infectious diseases transmission and public health systems, and treated less seriously than other ‘salient’ public health risks, having implications for how resources are allocated. Further, analysis suggests a weak alliance among different stakeholders, particularly policy makers and evidence generators, resulting in the continuation of traditional practices of infectious diseases surveillance without consideration of the impacts of climate change.
Conclusions
We argue that along with strengthening systemic issues (epidemiological capacity, data quality and inter-sectoral collaboration), it is necessary to build a stronger political commitment to urgently address the influence of climate change as a present and exponential risk factor in the spread of infectious disease in Nepal.
... In a future scenario, FL could enable the creation of AI models that learn from vast amounts of data generated by these systems across the globe. Tese models could predict disease progression and trigger alerts, enabling timely interventions and reducing the burden on healthcare systems during pandemics [81]. 6.7. ...
The intersection of Federated Learning (FL) and Healthcare 5.0 promises a transformative shift towards a more resilient future, particularly concerning pandemic preparedness. Within this context, Healthcare 5.0 signifies a holistic approach to healthcare delivery, where interconnected technologies enable data-driven decision-making, patient-centric care, and enhanced efficiency. This paper provides an in-depth exploration of FL’s role within the framework of Healthcare 5.0 and its implications for the pandemic response. Specifically, FL offers the potential to revolutionize pandemic preparedness within Healthcare 5.0 in several vital ways: it enables collaborative learning from distributed data sources without compromising individual data privacy, facilitates decentralized decision-making by empowering local healthcare institutions to contribute to a collective knowledge pool, and enhances real-time surveillance, enabling early detection of outbreaks and informed responses. We start by laying out the concepts of FL and Healthcare 5.0, followed by an analysis of current pandemic preparedness and response mechanisms. We delve into FL’s applications and case studies in healthcare, highlighting its potential benefits, including privacy protection, decentralized decision-making, and implementation challenges. By articulating how FL fits into Healthcare 5.0, we envisage future applications in a technologically integrated health system. By examining current applications and case studies of FL in healthcare, we highlight its potential benefits, including enhanced privacy protection and more effective decision support systems. Our findings demonstrate that FL can significantly improve pandemic response times and accuracy. Moreover, we speculate on the potential scenarios where FL could enhance pandemic preparedness and make healthcare more resilient. Finally, we recommend that policymakers, technologists, and educators address potential challenges and maximize the benefits of FL in Healthcare 5.0. This paper aims to contribute to the discourse on next-generation healthcare technologies, emphasizing FL’s potential to shape a more resilient healthcare future.
... Climate-based EWS can alert communities and health authorities about the increased risk of disease outbreaks during specific climate conditions, allowing for timely interventions and preventive measures [28]. Climate-based EWS for infectious diseases are in place across the world, for example, there are ENSO-based EWS for hantavirus pulmonary syndrome, Rift Valley Fever, cholera, and dengue [134,135]. The potential for climate-based EWS in the PICTs has been discussed and establishment frameworks proposed [136]. ...
Background
Climate induced changes in water-related infectious disease (WRID) transmission are a growing public health concern. The effects of climate on disease vary regionally, as do key socioeconomic modifiers. Regional syntheses are necessary to develop public health tools like risk maps and early warning systems at this scale. There is a high burden of WRID in the Pacific Island Countries and Territories (PICTs). There has been significant work on this topic in the PICTs, however, to date, there has been no regional systematic review of climate variability and WRID.
Methods
We searched the PubMed, Web of Science and Scopus scientific databases in September 2022 using a combination of disease, climate, and country terms. We included studies that evaluated the association between climate or weather variability and a WRID in the PICTs using a quantitative epidemiological design. We assessed risk of bias using validated tools. We analysed spatiotemporal publication patterns, synthesised the outcomes of studies in relation to the international literature and identified missing evidence.
Results & discussion
We identified 45 studies of climate and malaria, dengue, diarrhoea, leptospirosis, and typhoid, which represent major WRIDs of concern in the Pacific Islands. More than half of the studies were set in Papua New Guinea or Fiji. The number of studies published each year increased exponentially over time from the 1980s to present. We found few comparable outcomes per disease and setting across epidemiological studies which limited the potential for meta-analysis. However, we identified consistent increased incidence of diarrhoea, dengue, leptospirosis, and typhoid following extreme weather events, highlighting the necessity for adequate water, sanitation, and hygiene access across the PICTs. Additionally, there were consistent positive associations between temperature and dengue incidence in New Caledonia, highly seasonal malaria in PNG, increased diarrhoea incidence with high and low rainfall, and positive associations between leptospirosis and rainfall. These findings are biologically plausible and consistent with the international literature. Future work on this topic in the PICTs can take advantage of increasingly available health and climate data to consolidate the field across a greater diversity of settings and apply these findings to strengthening climate resilient health systems.
Registration
This review is registered with the international prospective register of systematic reviews (PROSPERO CRD42022353853 ), in accordance with PRISMA guidelines.
... However, as outbreaks are hard to anticipate, control efforts often start too late. Early warning systems have been developed to predict when and where outbreaks will start [3]. These typically depend on the statistical association between the risk of an outbreak and predictive variables. ...
To reduce the consequences of infectious disease outbreaks, the timely implementation of public health measures is crucial. Currently used early-warning systems are highly context-dependent and require a long phase of model building. A proposed solution to anticipate the onset or termination of an outbreak is the use of so -called resilience indicators. These indicators are based on the generic theory of critical slowing down and require only incidence time series. Here we assess the potential for this approach to contribute to outbreak anticipation. We systematically reviewed studies that used resilience indicators to predict outbreaks or terminations of epidemics. We identified 37 studies meeting the inclusion criteria: 21 using simulated data and 16 real-world data. 36 out of 37 studies detected significant signs of critical slowing down before a critical transition (i.e., the onset or end of an outbreak), with a highly variable sensitivity (i.e., the proportion of true positive outbreak warnings) ranging from 0.03 to 1 and a lead time ranging from 10 days to 68 months. Challenges include low resolution and limited length of time series, a too rapid increase in cases, and strong seasonal patterns which may hamper the sensitivity of resilience indicators. Alternative types of data, such as Google searches or social media data, have the potential to improve predictions in some cases. Resilience indicators may be useful when the risk of disease outbreaks is changing gradually. This may happen, for instance, when pathogens become increasingly adapted to an environment or evolve gradually to escape immunity. High-resolution monitoring is needed to reach sufficient sensitivity. If those conditions are met, resilience indicators could help improve the current practice of prediction, facilitating timely outbreak response. We provide a step-by-step guide on the use of resilience indicators in infectious disease epidemiology, and guidance on the relevant situations to use this approach.
... In this sense, the development of robust health information systems can substantially improve data quality and availability, a viewpoint proposed by Ranga et al. (2020). Given predicted climate changes, embedding climate adaptation in public health policies is crucial, as illustrated by successful global early warning systems (Morin et al., 2018). Sustainable urban planning can mitigate risks associated with rapid urbanisation . ...
Climate change and shifts in land use/ land cover (LULC) are critical factors affecting the environmental, societal, and health landscapes, notably influencing the spread of infectious diseases. This study delves into the intricate relationships between climate change, LULC alterations, and the prevalence of vector-borne and waterborne diseases in Coimbatore district, Tamil Nadu, India, between 1985 and 2015. The research utilised Landsat-4, Landsat-5, and Landsat-8 data to generate LULC maps, applying the maximum likelihood algorithm to highlight significant transitions over the years. This study revealed that built-up areas have increased by 67%, primarily at the expense of agricultural land, which was reduced by 51%. Temperature and rainfall data were obtained from APHRODITE Water Resources, and with a statistical analysis of the time series data revealed an annual average temperature increase of 1.8 °C and a minor but statistically significant rainfall increase during the study period. Disease data was obtained from multiple national health programmes, revealing an increasing trend in den-gue and diarrhoeal diseases over the study period. In particular, dengue cases surged, correlating strongly with the increase in built-up areas and temperature. public health, urban planning, and climate change mitigation. Amidst limited research on the intercon-nections among infectious diseases, climate change, and LULC changes in India, our study serves as a significant precursor for future management strategies in Coimbatore and analogous regions.
... Reactive/passive warnings: disease surveillance Reactive warning systems do not monitor the drivers or factors leading to new or re-emergent infectious diseases, but only the diseases themselves, often only once they have reached the human populations, but also, at times, the animal populations (Morin et al., 2018). They can be correlated with strategies that predict the further spread of viruses and passive/reactive strategies dedicated to stopping and reducing further transmission. ...
... Overall, most warning systems (80%) within the database followed a reactive approach, also known as a passive approach (Morin et al., 2018). They monitor the threat only once it has reached a detectable threshold within the population and can only be linked with reactive strategies aiming to reduce disease transmission. ...
Socio-economic, environmental, and ecological factors, as well as several natural hazards, have repeatedly been shown to drive emerging infectious disease risk. However, these drivers are largely excluded from surveillance, warning, and response systems. This paper identifies, analyses, and categorises 64 warning and response systems for infectious diseases. It finds that 80% of them are ‘reactive’ – they wait for disease outbreaks before issuing an alert and implementing mitigating strategies. Only 6% of the warning and response systems were ‘prevention-centred’. These both monitored and were linked to strategies that addressed drivers of disease emergence and re-emergence. This paper argues that warning systems’ failure to conceptualise emerging infectious diseases as part of an integrated human, animal, and environmental system stems from inadequate multi-sectoral collaboration and governance, compounded by barriers to data sharing and integration. This paper reviews existing approaches and frameworks that could help to build and expand prevention-centred warning and response systems. It also makes recommendations to foster multi-sectoral collaboration in governance and warning systems for infectious diseases. This includes proposing solutions to address compartmentalisation in international agreements, developing One Health national focal points and expanding bottom-up initiatives.
... The leading environmental factors were assumed to generate permissive conditions for host populations to increase and for viral infection to spread among hosts, making spillover to humans more likely [13]. If true, and the environmental conditions were identified rapidly enough, it should be possible to intervene and mitigate the outbreaks, especially for agents spilling over from wildlife [14]. ...
... Currently, outbreaks are identified and then responded to using confirmed reports of diseased humans or domestic animals once a detectable threshold is reached. These delays produce burdens on local or regional health care and economic systems [14]. Methods for implementing forecasts presume there is a staging of population level interventions to intervene more cheaply and efficiently [14]. ...
... These delays produce burdens on local or regional health care and economic systems [14]. Methods for implementing forecasts presume there is a staging of population level interventions to intervene more cheaply and efficiently [14]. As summarized in (Figure 1), the advantage of hypothetical forecasting systems is to reduce the duration and magnitude of outbreaks and mitigate impacts on health care infrastructure. ...
Hantaviral diseases have been recognized as 'place diseases' from their earliest identification and, epidemiologically, are tied to single host species with transmission occurring from infectious hosts to humans. As such, human populations are most at risk when they are in physical proximity to suitable habitats for reservoir populations, when numbers of infectious hosts are greatest. Because of the lags between improving habitat conditions and increasing infectious host abundance and spillover to humans, it should be possible to anticipate (forecast) where and when outbreaks will most likely occur. Most mammalian hosts are associated with specific habitat requirements, so identifying these habitats and the ecological drivers that impact population growth and the dispersal of viral hosts should be markers of the increased risk for disease outbreaks. These regions could be targeted for public health and medical education. This paper outlines the rationale for forecasting zoonotic outbreaks, and the information that needs to be clarified at various levels of biological organization to make the forecasting of orthohantaviruses successful. Major challenges reflect the transdisciplinary nature of forecasting zoonoses, with needs to better understand the implications of the data collected, how collections are designed, and how chosen methods impact the interpretation of results.
... As climate change is projected to increase temperature and cause variations in rainfall, integrating climate change adaptation measures into public health policy and planning is also very crucial. In this context, the development of early warning systems for disease outbreaks linked to weather events is important to consider as developed in various regions of the world (Morin et al. 2018). Furthermore, the implementation of sustainable land use policies to mitigate the effects of rapid urbanisation must also be considered as a priority. ...
Climate change, which encompasses variations in rainfall and temperature patterns, coupled with changes in land use/land cover (LULC), significantly impacts both the environment and society. These two factors, climate change and LULC shifts, have markedly affected human health, both directly and indirectly. Monitoring regional climate patterns, LULC changes, and disease outbreaks is crucial to ensure healthy living standards through a sustainable environment. This study investigates the correlation between climate change, LULC change, and the prevalence of infectious diseases transmitted by vectors and waterborne pathogens in Coimbatore district, Tamil Nadu, India, from 1985 to 2015. The study used Landsat-4, Landsat-5 and Landsat-8 data to generate LULC maps of the study area. The maximum likelihood algorithm facilitated the creation of these maps and detected changes for the years 1985, 2000, 2009, and 2015. Rainfall and temperature data for the study area were sourced from APHRODITE's Water Resources, and statistical analysis was applied to analyse these time series data. Infectious disease data was obtained from the Indian Council of Medical Research (ICMR), the Integrated Disease Surveillance Programme (IDSP), the National Vector Borne Disease Control Programme (NVBDCP), and the National Health System Resource Centre. These data were examined to identify trends in the occurrence of infectious diseases. The key findings of the study include (1) an overall increase in temperature and minor variations in rainfall in the study area during the study period; (2) an evident increase in built-up areas, as depicted by the LULC maps, attributable to industrialisation and population growth; (3) an emergence of dengue during the study period. The increasing patterns of vector-borne and water-borne diseases could be associated with changes in LULC and climate change. Given that the relationship between infectious diseases and their links to climate change and LULC changes has not been extensively researched in the Indian context, this study intends to contribute to a deeper understanding and delineation of future strategies in Coimbatore, India.
... 3 76 help build adaptive capacity and climate-resilient health systems. The early warning lead time ranges from 1-10 days where predictions are based on short-term weather forecasts, 1-6 months when based on seasonal forecasts, to multi-decadal based on climate change projections and the frequency of El Niño events (Morin et al., 2018). ...
... One example of an operational integrated disease early warning system is the Vibrio Map Viewer developed by the ECDC. This uses real-time data on sea surface temperature and the salinity of coastal waters to forecast the growth of pathogenic Vibrio species around the world with a five-day lead time (Morin et al., 2018). The forecasts are published in ECDC's Communicable Disease Threat Reports and shared with public health decision makers in Europe, who then have options for an appropriate response, including temporary restrictions to public beach access, safety alerts, and the notification of healthcare providers and at-risk populations (Morin et al., 2018). ...
... This uses real-time data on sea surface temperature and the salinity of coastal waters to forecast the growth of pathogenic Vibrio species around the world with a five-day lead time (Morin et al., 2018). The forecasts are published in ECDC's Communicable Disease Threat Reports and shared with public health decision makers in Europe, who then have options for an appropriate response, including temporary restrictions to public beach access, safety alerts, and the notification of healthcare providers and at-risk populations (Morin et al., 2018). ...
This report explores 10 transboundary climate risks of global importance. The assessments take a deep dive into how transboundary climate risks impact local livelihoods, and critical sectors such as finance, health and global supply chains (e.g. agricultural commodities and manufacturing components). Each chapter explores, for a given transboundary climate risk, its likelihood in a changing climate, its impacts across different distances and time scales, and its method of transmission, as well as provides readers with an in-depth analysis of a representative real-world case study.
... The early health warning system is a strong and reliable approach that has been proven to be an effective tool to provide the climate-health profile of a possible outbreak at least 2 weeks in advance [55]. Prior studies suggested that utilizing the available ambient and environment data that are closely related with the dynamics of disease (i.e., temperature, precipitation, relative humidity, etc.) to the fullest extent can significantly increase the chance to protect and enhance community health through simulation and prediction [55,56]. ...
Background:
Diarrhea remains a common infectious disease caused by various risk factors in developing countries. This study investigated the incidence rate and temporal associations between diarrhea and meteorological determinants in five regions of Surabaya, Indonesia.
Method:
Monthly diarrhea records from local governmental health facilities in Surabaya and monthly means of weather variables, including average temperature, precipitation, and relative humidity from Meteorology, Climatology, and Geophysical Agency were collected from January 2018 to September 2020. The generalized additive model was employed to quantify the time lag association between diarrhea risk and extremely low (5th percentile) and high (95th percentile) monthly weather variations in the north, central, west, south, and east regions of Surabaya (lag of 0-2 months).
Result:
The average incidence rate for diarrhea was 11.4 per 100,000 during the study period, with a higher incidence during rainy season (November to March) and in East Surabaya. This study showed that the weather condition with the lowest diarrhea risks varied with the region. The diarrhea risks were associated with extremely low and high temperatures, with the highest RR of 5.39 (95% CI 4.61, 6.17) in the east region, with 1 month of lag time following the extreme temperatures. Extremely low relative humidity increased the diarrhea risks in some regions of Surabaya, with the highest risk in the west region at lag 0 (RR = 2.13 (95% CI 1.79, 2.47)). Extremely high precipitation significantly affects the risk of diarrhea in the central region, at 0 months of lag time, with an RR of 3.05 (95% CI 2.09, 4.01).
Conclusion:
This study identified a high incidence of diarrhea in the rainy season and in the deficient developed regions of Surabaya, providing evidence that weather magnifies the adverse effects of inadequate environmental sanitation. This study suggests the local environmental and health sectors codevelop a weather-based early warning system and improve local sanitation practices as prevention measures in response to increasing risks of infectious diseases.
