1
|
Servadio JL, Convertino M, Fiecas M, Muñoz‐Zanzi C. Weekly Forecasting of Yellow Fever Occurrence and Incidence via Eco-Meteorological Dynamics. GEOHEALTH 2023; 7:e2023GH000870. [PMID: 37885914 PMCID: PMC10599710 DOI: 10.1029/2023gh000870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/31/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Yellow Fever (YF), a mosquito-borne disease, requires ongoing surveillance and prevention due to its persistence and ability to cause major epidemics, including one that began in Brazil in 2016. Forecasting based on factors influencing YF risk can improve efficiency in prevention. This study aimed to produce weekly forecasts of YF occurrence and incidence in Brazil using weekly meteorological and ecohydrological conditions. Occurrence was forecast as the probability of observing any cases, and incidence was forecast to represent morbidity if YF occurs. We fit gamma hurdle models, selecting predictors from several meteorological and ecohydrological factors, based on forecast accuracy defined by receiver operator characteristic curves and mean absolute error. We fit separate models for data before and after the start of the 2016 outbreak, forecasting occurrence and incidence for all municipalities of Brazil weekly. Different predictor sets were found to produce most accurate forecasts in each time period, and forecast accuracy was high for both time periods. Temperature, precipitation, and previous YF burden were most influential predictors among models. Minimum, maximum, mean, and range of weekly temperature, precipitation, and humidity contributed to forecasts, with optimal lag times of 2, 6, and 7 weeks depending on time period. Results from this study show the use of environmental predictors in providing regular forecasts of YF burden and producing nationwide forecasts. Weekly forecasts, which can be produced using the forecast model developed in this study, are beneficial for informing immediate preparedness measures.
Collapse
Affiliation(s)
- Joseph L. Servadio
- Department of BiologyCenter for Infectious Disease DynamicsPennsylvania State UniversityUniversity ParkPAUSA
- Division of Environmental Health SciencesSchool of Public HealthUniversity of MinnesotaMinneapolisMNUSA
| | | | - Mark Fiecas
- Division of BiostatisticsSchool of Public HealthUniversity of MinnesotaMinneapolisMNUSA
| | - Claudia Muñoz‐Zanzi
- Division of Environmental Health SciencesSchool of Public HealthUniversity of MinnesotaMinneapolisMNUSA
| |
Collapse
|
2
|
Bradley EA, Lockaby G. Leptospirosis and the Environment: A Review and Future Directions. Pathogens 2023; 12:1167. [PMID: 37764975 PMCID: PMC10538202 DOI: 10.3390/pathogens12091167] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 08/31/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
Leptospirosis is a zoonotic disease of global importance with significant morbidity and mortality. However, the disease is frequently overlooked and underdiagnosed, leading to uncertainty of the true scale and severity of the disease. A neglected tropical disease, leptospirosis disproportionately impacts disadvantaged socioeconomic communities most vulnerable to outbreaks of zoonotic disease, due to contact with infectious animals and contaminated soils and waters. With growing evidence that Leptospira survives, persists, and reproduces in the environment, this paper reviews the current understanding of the pathogen in the environment and highlights the unknowns that are most important for future study. Through a systematic Boolean review of the literature, our study finds that detailed field-based study of Leptospira prevalence, survival, and transmission in natural waters and soils is lacking from the current literature. This review identified a strong need for assessment of physical characteristics and biogeochemical processes that support long-term viability of Leptospira in the environment followed by epidemiological assessment of the transmission and movement of the same strains of Leptospira in the present wildlife and livestock as the first steps in improving our understanding of the environmental stage of the leptospirosis transmission cycle.
Collapse
Affiliation(s)
- Elizabeth A. Bradley
- College of Forestry, Wildlife, and Environment, Auburn University, Auburn, AL 36849, USA
| | | |
Collapse
|
3
|
Teles AJ, Bohm BC, Silva SCM, Bruhn FRP. Socio-geographical factors and vulnerability to leptospirosis in South Brazil. BMC Public Health 2023; 23:1311. [PMID: 37420253 PMCID: PMC10329394 DOI: 10.1186/s12889-023-16094-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 06/09/2023] [Indexed: 07/09/2023] Open
Abstract
BACKGROUND Leptospirosis, caused by the Leptospira bacteria, is an acute infectious disease that is mainly transmitted by exposure to contaminated soil or water, thereby presenting a wide range of subsequent clinical conditions. This study aimed to assess the distribution of cases and deaths from leptospirosis and its association with social vulnerability in the state of Rio Grande do Sul, Brazil, between 2010 and 2019. METHODS The lethality rates and incidence of leptospirosis and their association with gender, age, education, and skin color were analyzed using chi-square tests. The spatial relationship between the environmental determinants, social vulnerability, and the incidence rate of leptospirosis in the different municipalities of Rio Grande do Sul was analyzed through spatial regression analysis. RESULTS During the study period, a total of 4,760 cases of leptospirosis, along with 238 deaths, were confirmed. The mean incidence rate was 4.06 cases/100,000 inhabitants, while the mean fatality rate was 5%. Although the entire population was susceptible, white-colored individuals, males, people of the working-age group, along with less-educated individuals, were more affected by the disease. Lethality was higher in people with dark skin, and the prime risk factor associated with death was the direct contact of the patients with rodents, sewage, and garbage. The social vulnerability was positively associated with the incidence of leptospirosis in the Rio Grande do Sul, especially in municipalities located in the center of the state. CONCLUSIONS It is evident that the incidence of the disease is significantly related to the vulnerability of the population. The use of the health vulnerability index showed great relevance in the evaluation of leptospirosis cases and can be used further as a tool to help municipalities identify disease-prone areas for intervention and resource allocation.
Collapse
Affiliation(s)
| | - Bianca Conrad Bohm
- Postgraduate Program in Veterinary, Federal University of Pelotas, Capão Do Leão, Rio Grande Do Sul, Brazil
| | - Suellen Caroline M Silva
- Postgraduate Program in Veterinary, Federal University of Pelotas, Capão Do Leão, Rio Grande Do Sul, Brazil.
| | - Fábio Raphael P Bruhn
- Department of Preventive Veterinary Medicine, Federal University of Pelotas, Capão Do Leão, Rio Grande Do Sul, Brazil
| |
Collapse
|
4
|
Maas M, de Vries A, Cuperus T, van der Giessen J, Kruisheer M, Janse I, Swart A. A predictive risk map for human leptospirosis guiding further investigations in brown rats and surface water. Infect Ecol Epidemiol 2023; 13:2229583. [PMID: 37398878 PMCID: PMC10308863 DOI: 10.1080/20008686.2023.2229583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 06/21/2023] [Indexed: 07/04/2023] Open
Abstract
Leptospirosis is a zoonosis caused by the spirochete Leptospira spp. It is often not clear why certain areas appear to be hotspots for human leptospirosis. Therefore, a predictive risk map for the Netherlands was developed and assessed, based on a random forest model for human leptospirosis incidence levels with various environmental factors and rat density as variables. Next, it was tested whether misclassifications of the risk map could be explained by the prevalence of Leptospira spp. in brown rats. Three recreational areas were chosen, and rats (≥25/location) were tested for Leptospira spp. Concurrently, it was investigated whether Leptospira spp. prevalence in brown rats was associated with Leptospira DNA concentration in surface water, to explore the usability of this parameter in future studies. Approximately 1 L of surface water sample was collected from 10 sites and was tested for Leptospira spp. Although the model predicted the locations of patients relatively well, this study showed that the prevalence of Leptospira spp. infection in rats may be an explaining variable that could improve the predictive model performance. Surface water samples were all negative, even if they had been taken at sites with a high Leptospira spp. prevalence in rats.
Collapse
Affiliation(s)
- Miriam Maas
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Ankje de Vries
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Tryntsje Cuperus
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Joke van der Giessen
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Matthijs Kruisheer
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Ingmar Janse
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Arno Swart
- Centre for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| |
Collapse
|
5
|
Ab Kadir MA, Abdul Manaf R, Mokhtar SA, Ismail LI. Spatio-Temporal Analysis of Leptospirosis Hotspot Areas and Its Association With Hydroclimatic Factors in Selangor, Malaysia: Protocol for an Ecological Cross-sectional Study. JMIR Res Protoc 2023; 12:e43712. [PMID: 37184897 DOI: 10.2196/43712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 04/27/2023] [Accepted: 04/30/2023] [Indexed: 05/16/2023] Open
Abstract
BACKGROUND Leptospirosis is considered a neglected zoonotic disease in temperate regions but an endemic disease in countries with tropical climates such as South America, Southern Asia, and Southeast Asia. There has been an increase in leptospirosis incidence in Malaysia from 1.45 to 25.94 cases per 100,000 population between 2005 and 2014. With increasing incidence in Selangor, Malaysia, and frequent climate change dynamics, a study on the disease hotspot areas and their association with the hydroclimatic factors would further enhance disease surveillance and public health interventions. OBJECTIVE This study aims to examine the association between the spatio-temporal distribution of leptospirosis hotspot areas from 2011 to 2019 with the hydroclimatic factors in Selangor using the geographical information system and remote sensing techniques to develop a leptospirosis hotspot predictive model. METHODS This will be an ecological cross-sectional study with geographical information system and remote sensing mapping and analysis concerning leptospirosis using secondary data. Leptospirosis cases in Selangor from January 2011 to December 2019 shall be obtained from the Selangor State Health Department. Laboratory-confirmed cases with data on the possible source of infection would be identified and georeferenced according to their longitude and latitudes. Topographic data consisting of subdistrict boundaries and the distribution of rivers in Selangor will be obtained from the Department of Survey and Mapping. The ArcGIS Pro software will be used to evaluate the clustering of the cases and mapped using the Getis-Ord Gi* tool. The satellite images for rainfall and land surface temperature will be acquired from the Giovanni National Aeronautics and Space Administration EarthData website and processed to obtain the average monthly values in millimeters and degrees Celsius. Meanwhile, the average monthly river hydrometric levels will be obtained from the Department of Drainage and Irrigation. Data are then inputted as thematic layers and in the ArcGIS software for further analysis. The artificial neural network analysis in artificial intelligence Phyton software will then be used to obtain the leptospirosis hotspot predictive model. RESULTS This research was funded as of November 2022. Data collection, processing, and analysis commenced in December 2022, and the results of the study are expected to be published by the end of 2024. The leptospirosis distribution and clusters may be significantly associated with the hydroclimatic factors of rainfall, land surface temperature, and the river hydrometric level. CONCLUSIONS This study will explore the associations of leptospirosis hotspot areas with the hydroclimatic factors in Selangor and subsequently the development of a leptospirosis predictive model. The constructed predictive model could potentially be used to design and enhance public health initiatives for disease prevention. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/43712.
Collapse
Affiliation(s)
- Muhammad Akram Ab Kadir
- Department of Community Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia
| | - Rosliza Abdul Manaf
- Department of Community Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia
| | - Siti Aisah Mokhtar
- Department of Community Health, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Selangor, Malaysia
| | - Luthffi Idzhar Ismail
- Department of Electrical & Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Selangor, Malaysia
| |
Collapse
|
6
|
Wang H, Galbraith E, Convertino M. Algal Bloom Ties: Spreading Network Inference and Extreme Eco-Environmental Feedback. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25040636. [PMID: 37190425 PMCID: PMC10138021 DOI: 10.3390/e25040636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/04/2023] [Accepted: 04/07/2023] [Indexed: 05/17/2023]
Abstract
Coastal marine ecosystems worldwide are increasingly affected by tide alterations and anthropogenic disturbances affecting the water quality and leading to frequent algal blooms. Increased bloom persistence is a serious threat due to the long-lasting impacts on ecological processes and services, such as carbon cycling and sequestration. The exploration of eco-environmental feedback and algal bloom patterns remains challenging and poorly investigated, mostly due to the paucity of data and lack of model-free approaches to infer universal bloom dynamics. Florida Bay, taken as an epitome for biodiversity and blooms, has long experienced algal blooms in its central and western regions, and, in 2006, an unprecedented bloom occurred in the eastern habitats rich in corals and vulnerable habitats. With global aims, we analyze the occurrence of blooms in Florida Bay from three perspectives: (1) the spatial spreading networks of chlorophyll-a (CHLa) that pinpoint the source and unbalanced habitats; (2) the fluctuations of water quality factors pre- and post-bloom outbreaks to assess the environmental impacts of ecological imbalances and target the prevention and control of algal blooms; and (3) the topological co-evolution of biogeochemical and spreading networks to quantify ecosystem stability and the likelihood of ecological shifts toward endemic blooms in the long term. Here, we propose the transfer entropy (TE) difference to infer salient dynamical inter actions between the spatial areas and biogeochemical factors (ecosystem connectome) underpinning bloom emergence and spread as well as environmental effects. A Pareto principle, defining the top 20% of areal interactions, is found to identify bloom spreading and the salient eco-environmental interactions of CHLa associated with endemic and epidemic regimes. We quantify the spatial dynamics of algal blooms and, thus, obtain areas in critical need for ecological monitoring and potential bloom control. The results show that algal blooms are increasingly persistent over space with long-term negative effects on water quality factors, in particular, about how blooms affect temperature locally. A dichotomy is reported between spatial ecological corridors of spreading and biogeochemical networks as well as divergence from the optimal eco-organization: randomization of the former due to nutrient overload and temperature increase leads to scale-free CHLa spreading and extreme outbreaks a posteriori. Subsequently, the occurrence of blooms increases bloom persistence, turbidity and salinity with potentially strong ecological effects on highly biodiverse and vulnerable habitats, such as tidal flats, salt-marshes and mangroves. The probabilistic distribution of CHLa is found to be indicative of endemic and epidemic regimes, where the former sets the system to higher energy dissipation, larger instability and lower predictability. Algal blooms are important ecosystem regulators of nutrient cycles; however, chlorophyll-a outbreaks cause vast ecosystem impacts, such as aquatic species mortality and carbon flux alteration due to their effects on water turbidity, nutrient cycling (nitrogen and phosphorus in particular), salinity and temperature. Beyond compromising the local water quality, other socio-ecological services are also compromised at large scales, including carbon sequestration, which affects climate regulation from local to global environments. Yet, ecological assessment models, such as the one presented, inferring bloom regions and their stability to pinpoint risks, are in need of application in aquatic ecosystems, such as subtropical and tropical bays, to assess optimal preventive controls.
Collapse
Affiliation(s)
- Haojiong Wang
- Laboratory of Information Communication Networks, Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan
| | - Elroy Galbraith
- Laboratory of Information Communication Networks, Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, Japan
| | - Matteo Convertino
- fuTuRE EcoSystems Lab (TREES), Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
- Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration, Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China
| |
Collapse
|
7
|
Llop MJ, Gómez A, Llop P, López MS, Müller GV. Prediction of leptospirosis outbreaks by hydroclimatic covariates: a comparative study of statistical models. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2022; 66:2529-2540. [PMID: 36306013 PMCID: PMC9614762 DOI: 10.1007/s00484-022-02378-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 08/25/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Leptospirosis, the infectious disease caused by a spirochete bacteria, is a major public health problem worldwide. In Argentina, some regions have climatic and geographical characteristics that favor the habitat of bacteria of the Leptospira genus, whose survival strongly depends on climatic factors, enhanced by climate change, which increase the problems associated with people's health. In order to have a method to predict leptospirosis cases, in this paper, five time series forecasting methods are compared: two parametric (autoregressive integrated moving average and an alternative one that allows covariates, ARIMA and ARIMAX, respectively), two nonparametric (Nadaraya-Watson Kernel estimator, one and two kernels versions, NW-1 K and NW-2 K), and one semiparametric (semi-functional partial linear regression, SFPLR) method. For this, the number of cases of leptospirosis registered from 2009 to 2020 in three important cities of northeastern Argentina is used, as well as hydroclimatic covariates related to the presence of cases. According to the obtained results, there is no method that improves considerably the rest and can be recommended as a unique tool for leptospirosis prediction. However, in general, the NW-2 K method gets a better performance. This work, in addition to using a long-term high-quality time series, enriches the area of applications of statistical models to epidemiological leptospirosis data by the incorporation of hydroclimatic variables, and it is recommended directing further efforts in this line of research, under the context of current climate change.
Collapse
Affiliation(s)
- María José Llop
- Facultad de Ingeniería Química, Universidad Nacional del Litoral (UNL), Santa Fe, Argentina.
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Santa Fe, Argentina.
| | - Andrea Gómez
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Santa Fe, Argentina
- CEVARCAM, Facultad de Ingeniería y Ciencias Hídricas, Universidad Nacional del Litoral (UNL), Santa Fe, Argentina
| | - Pamela Llop
- Facultad de Ingeniería Química, Universidad Nacional del Litoral (UNL), Santa Fe, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Santa Fe, Argentina
| | - María Soledad López
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Santa Fe, Argentina
- CEVARCAM, Facultad de Ingeniería y Ciencias Hídricas, Universidad Nacional del Litoral (UNL), Santa Fe, Argentina
| | - Gabriela V Müller
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Santa Fe, Argentina
- CEVARCAM, Facultad de Ingeniería y Ciencias Hídricas, Universidad Nacional del Litoral (UNL), Santa Fe, Argentina
| |
Collapse
|
8
|
Galbraith E, Li J, Rio-Vilas VJD, Convertino M. In.To. COVID-19 socio-epidemiological co-causality. Sci Rep 2022; 12:5831. [PMID: 35388071 PMCID: PMC8986029 DOI: 10.1038/s41598-022-09656-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 03/11/2022] [Indexed: 11/09/2022] Open
Abstract
Social media can forecast disease dynamics, but infoveillance remains focused on infection spread, with little consideration of media content reliability and its relationship to behavior-driven epidemiological outcomes. Sentiment-encoded social media indicators have been poorly developed for expressed text to forecast healthcare pressure and infer population risk-perception patterns. Here we introduce Infodemic Tomography (InTo) as the first web-based interactive infoveillance cybertechnology that forecasts and visualizes spatio-temporal sentiments and healthcare pressure as a function of social media positivity (i.e., Twitter here), considering both epidemic information and potential misinformation. Information spread is measured on volume and retweets, and the Value of Misinformation (VoMi) is introduced as the impact on forecast accuracy where misinformation has the highest dissimilarity in information dynamics. We validated InTo for COVID-19 in New Delhi and Mumbai by inferring distinct socio-epidemiological risk-perception patterns. We forecast weekly hospitalization and cases using ARIMA models and interpolate spatial hospitalization using geostatistical kriging on inferred risk perception curves between tweet positivity and epidemiological outcomes. Geospatial tweet positivity tracks accurately [Formula: see text]60[Formula: see text] of hospitalizations and forecasts hospitalization risk hotspots along risk aversion gradients. VoMi is higher for risk-prone areas and time periods, where misinformation has the highest non-linear predictability, with high incidence and positivity manifesting popularity-seeking social dynamics. Hospitalization gradients, VoMi, effective healthcare pressure and spatial model-data gaps can be used to predict hospitalization fluxes, misinformation, healthcare capacity gaps and surveillance uncertainty. Thus, InTo is a participatory instrument to better prepare and respond to public health crises by extracting and combining salient epidemiological and social surveillance at any desired space-time scale.
Collapse
Affiliation(s)
- Elroy Galbraith
- Nexus Group, Faculty and Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan
| | - Jie Li
- Nexus Group, Faculty and Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan.,Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands
| | | | - Matteo Convertino
- fuTuRE EcoSystems Lab, Institute of Environment and Ecology, Tsinghua SIGS, Tsinghua University, Shenzhen, China. .,Tsinghua Shenzhen International Graduate School, University Town of Shenzhen, Tsinghua Park, Nanshan District, Shenzhen, 518055, China.
| |
Collapse
|