Remote sensing, land cover changes, and vector-borne diseases: use of high spatial resolution satellite imagery to map the risk of occurrence of cutaneous leishmaniasis in Ghardaïa, Algeria

Modern technologies and solutions to enhance surveillance and response systems for emerging zoonotic diseases

Background

Zoonotic diseases originating in animals pose a significant threat to global public health. Recent outbreaks, such as coronavirus disease 2019 (COVID-19), have caused widespread illness, death, and socioeconomic disruptions worldwide. To cope with these diseases effectively, it is crucial to strengthen surveillance capabilities and establish rapid response systems.

Aim

The aim of this review to examine the modern technologies and solutions that have the potential to enhance zoonotic disease surveillance and outbreak responses and provide valuable insights into how cutting-edge innovations could be leveraged to prevent, detect, and control emerging zoonotic disease outbreaks. Herein, we discuss advanced tools including big data analytics, artificial intelligence, the Internet of Things, geographic information systems, remote sensing, molecular diagnostics, point-of-care testing, telemedicine, digital contact tracing, and early warning systems.

Results

These technologies enable real-time monitoring, the prediction of outbreak risks, early anomaly detection, rapid diagnosis, and targeted interventions during outbreaks. When integrated through collaborative partnerships, these strategies can significantly improve the speed and effectiveness of zoonotic disease control. However, several challenges persist, particularly in resource-limited settings, such as infrastructure limitations, costs, data integration and training requirements, and ethical implementation.

Conclusion

With strategic planning and coordinated efforts, modern technologies and solutions offer immense potential to bolster surveillance and outbreak responses, and serve as a critical resource against emerging zoonotic disease threats worldwide.

Handheld Ultra-Fast Duplex Polymerase Chain Reaction Assays and Lateral Flow Detection and Identification of Leishmania Parasites for Cutaneous Leishmaniases Diagnosis

Early and accurate detection of infectious diseases is a key step for surveillance, epidemiology and control, notably timely disease diagnosis, patient management and follow-up. In this study, we aimed to develop handheld ultra-fast duplex PCR assays coupled to amplicon detection by lateral flow (LF) immunoassay to deliver a rapid and simple molecular diagnostic test for concomitant detection and identification of the main Leishmania parasites encountered in Tunisia. We selected two DNA targets to amplify L. major/L. tropica and L. infantum/L. tropica groups of species DNAs, respectively. We optimized the experimental conditions of a duplex ultra-fast PCR. The amplification is performed using a portable Palm convection PCR machine within 18 min, and the products are detected using an LF cassette within 10 min. The test allows the identification of the infecting species according to the position and number of test lines revealed. Tested on a selection of DNAs of representative Leishmania strains of the three studied species (N = 37), the ultra-fast duplex PCR-LF showed consistent, stable and reproducible results. The analytical limit of detection of the test was 0.4 pg for L. major, 4 pg for L. infantum and 40 pg for L. tropica.

Animal leishmaniasis in Algeria: A systematic review and meta-analysis

Leishmaniasis is a neglected tropical parasitic vector-borne disease that has emerged or re-emerged in recent years and is a major health problem. Algeria is a country where leishmaniasis reaches high levels of endemicity. This systematic review and meta-analysis was performed to collect, compile and summarise data on the prevalence of animal leishmaniasis in Algeria. Data were collected from 2004 to 2022 during which a number of 12 papers were published from dogs, cats, hedgehogs, and jackals. A total of 4812 dogs, 388 stray cats, 24 hedgehogs and 2 Jackals were included in this analysis. The diagnosis of leishmaniasis was performed using serological or molecular tests. The overall prevalence in dogs was 21.2 % (95 %CI, 15.7-26.9 %), and in stray cats, hedgehogs and Jackal was, respectively, 25.3 % (95 %CI, 17-36.6 %), 20.8 % (95 %CI, 8.8-40.9 %) and 50 %. This study is a comprehensive epidemiological analysis of canine leishmaniasis in Algeria and will therefore be a useful tool for researchers. Further studies are needed for a better and continuous assessment of the occurrence of this zoonosis. It can also be used to propose or improve appropriate national surveillance programs for these diseases.

Dipeptidyl peptidase III as a DNA marker to investigate epidemiology and taxonomy of Old World Leishmania species

BACKGROUND

Dipeptidyl peptidase III (DPPIII) member of M49 peptidase family is a zinc-dependent metallopeptidase that cleaves dipeptides sequentially from the N-terminus of its substrates. In Leishmania, DPPIII, was reported with other peptidases to play a significant role in parasites' growth and survival. In a previous study, we used a coding sequence annotated as DPPIII to develop and evaluate a PCR assay that is specific to dermotropic Old World (OW) Leishmania species. Thus, our objective was to further assess use of this gene for Leishmania species identification and for phylogeny, and thus for diagnostic and molecular epidemiology studies of Old World Leishmania species.

METHODOLOGY

Orthologous DDPIII genes were searched in all Leishmania genomes and aligned to design PCR primers and identify relevant restriction enzymes. A PCR assays was developed and seventy-two Leishmania fragment sequences were analyzed using MEGA X genetics software to infer evolution and phylogenetic relationships of studied species and strains. A PCR-RFLP scheme was also designed and tested on 58 OW Leishmania strains belonging to 8 Leishmania species and evaluated on 75 human clinical skin samples.

FINDINGS

Sequence analysis showed 478 variable sites (302 being parsimony informative). Test of natural selection (dN-dS) (-0.164, SE = 0.013) inferred a negative selection, characteristic of essential genes, corroborating the DPPIII importance for parasite survival. Inter- and intra-specific genetic diversity was used to develop universal amplification of a 662bp fragment. Sequence analyses and phylogenies confirmed occurrence of 6 clusters congruent to L. major, L. tropica, L. aethiopica, L. arabica, L. turanica, L. tarentolae species, and one to the L. infantum and L. donovani species complex. A PCR-RFLP algorithm for Leishmania species identification was designed using double digestions with HaeIII and KpnI and with SacI and PvuII endonucleases. Overall, this PCR-RFLP yielded distinct profiles for each of the species L. major, L. tropica, L. aethiopica, L. arabica and L. turanica and the L. (Sauroleishmania) L. tarentolae. The species L. donovani, and L. infantum shared the same profile except for strains of Indian origin. When tested on clinical samples, the DPPIII PCR showed sensitivities of 82.22% when compared to direct examination and was able to identify 84.78% of the positive samples.

CONCLUSION

The study demonstrates that DPPIII gene is suitable to detect and identify Leishmania species and to complement other molecular methods for leishmaniases diagnosis and epidemiology. Thus, it can contribute to evidence-based disease control and surveillance.

An investigation of the effects of environmental and ecologic factors on cutaneous leishmaniasis in the old world: a systematic review study

OBJECTIVES

Leishmaniasis is a neglected and widespread parasitic disease that can lead to serious health problems. The current review study aimed to synthesize the relationship between ecologic and environmental factors (e.g., weather conditions, climatology, temperature and topology) and the incidence of cutaneous leishmaniasis (CL) in the Old World.

CONTENT

A systematic review was conducted based on English, and Persian articles published from 2015 to 2020 in PubMed/Medline, Science Direct, Web of Science and Google Scholar. Keywords used to search articles were leishmaniasis, environmental factors, weather condition, soil, temperature, land cover, ecologic* and topogr*. All articles were selected and assessed for eligibility according to the titles or abstracts. The quality screening process of articles was carried out by two independent authors. The selected articles were checked according to the inclusion and exclusion criteria.

SUMMARY AND OUTLOOK

A total of 827 relevant records in 2015-2020 were searched and after evaluating the articles, 23 articles met the eligibility criteria; finally, 14 full-text articles were included in the systematic review. Two different categories of ecologic/environmental factors (weather conditions, temperature, rainfall/precipitation and humidity) and land characteristics (land cover, slope, elevation and altitude, earthquake and cattle sheds) were the most important factors associated with CL incidence.

CONCLUSIONS

Temperature and rainfall play an important role in the seasonal cycle of CL as many CL cases occurred in arid and semiarid areas in the Old World. Moreover, given the findings of this study regarding the effect of weather conditions on CL, it can be concluded that designing an early warning system is necessary to predict the incidence of CL based on different weather conditions.

Measuring Human and Economic Activity From Satellite Imagery to Support City-Scale Decision-Making During COVID-19 Pandemic

The COVID-19 outbreak forced governments worldwide to impose lockdowns and quarantines to prevent virus transmission. As a consequence, there are disruptions in human and economic activities all over the globe. The recovery process is also expected to be rough. Economic activities impact social behaviors, which leave signatures in satellite images that can be automatically detected and classified. Satellite imagery can support the decision-making of analysts and policymakers by providing a different kind of visibility into the unfolding economic changes. In this article, we use a deep learning approach that combines strategic location sampling and an ensemble of lightweight convolutional neural networks (CNNs) to recognize specific elements in satellite images that could be used to compute economic indicators based on it, automatically. This CNN ensemble framework ranked third place in the US Department of Defense xView challenge, the most advanced benchmark for object detection in satellite images. We show the potential of our framework for temporal analysis using the US IARPA Function Map of the World (fMoW) dataset. We also show results on real examples of different sites before and after the COVID-19 outbreak to illustrate different measurable indicators. Our code and annotated high-resolution aerial scenes before and after the outbreak are available on GitHub.1.https://github.com/maups/covid19-satellite-analysis.

Updates on Geographical Dispersion of Leishmania Parasites Causing Cutaneous Affections in Algeria

Leishmaniases are neglected tropical diseases of public health concern in Algeria. To update the geographical distribution of Leishmania spp. causing cutaneous affection, we examined a set of Giemsa-stained smears prepared from skin lesions of the patients suspected to have cutaneous leishmaniasis (CL) in various geographical areas in Algeria. The identification of Leishmania parasites was performed using microscopy, conventional PCR, and PCR-RFLP (PCR-Restriction Fragment Length Polymorphism) targeting ITS1-rDNA. Among 32 smears provided from 27 suspected patients with cutaneous lesions, no trace of parasites was observed in the smear of three patients using microscopy and molecular approaches. Furthermore, four patients presented at least two lesions. PCR-RFLP confirmed the presence of Leishmania in 29 smears prepared from 24 patients. Two biopsies, negative after microscopic examination, were found positive by PCR. Of these 29 PCR positive smears (24 patients), 20 were identified using RFLP-PCR as L. major, two as L. tropica, and two as L. infantum. We found L. major infected patients from Ain skhouna, Biskra, El M'hir, Ghardaïa, M'Sila, and Saida, in agreement with previously reported cases. Furthermore, we highlighted for the first time, the identification of L. major in the patients from Bourkika, Bou Kremissa, Bou Saada Clef, Hajout, Maghnia, Médéa, Menaceur, Messad, Mostaghanem, Nador, Oran, and Sidi Okba. A phylogenetic reconstruction performed with sequences collected from the PCR products confirmed these identifications. Our data provide additional information on the geographical extension of CL caused by L. tropica and L. infantum in Algeria.

Modeling the Ecological Niche: A Case Study on Bioclimatic Factors Related to the Distribution of Phlebotomus tobbi Adler & Theodor (Diptera: Psychodidae) in Two Endemic Foci of Adana

Cutaneous leishmaniasis (CL) is a zoonotic infectious disease caused mainly by Leishmania infantum Nicolle, 1908 (Kinetoplastida: Trypanosomatida) transmitted by dominant species Phlebotomus tobbi Adler & Theodor (Diptera: Psychodidae) in Adana, Turkey. CL has been reported to be detected commonly in low-socioeconomic status population scattered in rural areas. The environmental determinants are relatively poorly understood, especially in Adana despite the fact that Adana is endemic foci of CL. The subject of this study was the current and future probability model of P. tobbi in the study areas, and to determine the underlying factors affecting its distribution. Sticky papers and CDC light traps were used for capturing the sand fly specimens. The current and future presence of P. tobbi was modeled using maximum entropy (MaxEnt) techniques. The predictive model indicated the presence of P. tobbi in the southeast and south part of the selected study area with 0.816 area under the curve (AUC) value. The model also implied that its survival could tend to expand with suitable climatic conditions in future (2070) with 0.798 AUC value. In addition, aspect, digital elevation model, BIO3, BIO 10, BIO12, and BIO14 were determined as the most influential variables for current and projected future. ArcGIS and MaxEnt software were used for the ecological niche model analysis to explore the ecological conditions of the disease. I suggest that produced models contribute to better understanding of epidemiology and controlling of vector-borne diseases.

Distribution pattern of anthroponotic cutaneous leishmaniasis caused by Leishmania tropica in Western Afghanistan during 2013-2014

Anthroponotic cutaneous leishmaniasis (ACL), caused by Leishmania tropica, is the main cause of cutaneous leishmaniasis (CL) in the Herat province, Western Afghanistan. We investigated the role of environmental factors on ACL distribution in Herat. Epidemiological data from 2457 patients were retrieved from the local WHO sub-office. Shapefile layers of districts, cities, villages, land cover, soil type and digital elevation model (DEM) of the Herat province were used to assess, by logistic regression modelling, the effects of land cover, soil types, elevation, and proximity to the Harirud river on the distribution of ACL. The key determinants of distribution were: (i) close proximity to the Harirud river, (ii) elevation between 700 and 1200m, (iii) intensive and intermittent irrigated cultivated land, and (iv) Haplocalcids with Torriorthents and Torrifluvents soil types. No ACL cases were found below 700m, and a few cases were present at >1200m in irrigated areas around the Harirud river. These findings suggest that moist soil and the humidity from irrigated areas found between 700 and 1200m provide suitable breeding sites of Phlebotomus sergenti, the main sandfly vector of L. tropica in Afghanistan. The effect of elevation also explains the predominance of ACL over ZCL in this region. The present study showed that distribution of ACL is strongly associated with environmental factors in West Afghanistan where the political and socio-economic conditions may also affect the epidemiology of CL.

Satellite Remote Sensing for Coastal Management: A Review of Successful Applications

Management of coastal and marine natural resources presents a number of challenges as a growing global population and a changing climate require us to find better strategies to conserve the resources on which our health, economy, and overall well-being depend. To evaluate the status and trends in changing coastal resources over larger areas, managers in government agencies and private stakeholders around the world have increasingly turned to remote sensing technologies. A surge in collaborative and innovative efforts between resource managers, academic researchers, and industry partners is becoming increasingly vital to keep pace with evolving changes of our natural resources. Synoptic capabilities of remote sensing techniques allow assessments that are impossible to do with traditional methods. Sixty years of remote sensing research have paved the way for resource management applications, but uncertainties regarding the use of this technology have hampered its use in management fields. Here we review examples of remote sensing applications in the sectors of coral reefs, wetlands, water quality, public health, and fisheries and aquaculture that have successfully contributed to management and decision-making goals.

Environmental risk modelling and potential sand fly vectors of cutaneous leishmaniasis in Chitral district: a leishmanial focal point of mount Tirich Mir, Pakistan

OBJECTIVE

To provide baseline information about suspected vectors and the incidence, distribution and an active zone of transmission for cutaneous leishmaniasis (CL) in Chitral, Pakistan, using GIS tools; and to investigate the role of environmental factors in the disease dynamics.

METHOD

Two surveys in 2014 and 2016 as a basis for choropleth and environmental risk mapping.

RESULTS

A total of 769 captured specimens yielded 14 Phlebotomus and six Sergentomyia species including two potential vectors of CL, i.e. Phlebotomus papatasi and Phlebotomus sergenti. P. papatasi (71%) was dominant, followed by P. sergenti (18%). A choropleth map generated in Arcmap 10.1 based on 1560 CL case reports displayed maximum prevalence (0.92-2.5%) in Ayun, Broz, Charun, Chitral 1 and 2 and Darosh 1 and 2 union councils. An environmental risk map constructed by MaxEnt 3.3.3 defined an active zone of transmission based on leishmaniasis occurrence records (n = 315). The analysis of variable contribution in MaxEnt indicates significance of elevation (54.4%), population density (23.3%) and land use/land cover (6.6%) in CL disease dynamics.

CONCLUSION

The probability of CL increases (0.6-1 on logistic scale) in severely deforested areas, in lowland valleys and in regions with high-population density.

A time series analysis of environmental and metrological factors impact on cutaneous leishmaniasis incidence in an endemic area of Dehloran, Iran

The aim of this study was to investigate the relationship between the environmental and metrological variables and cutaneous leishmaniasis (CL) transmission and its prediction in a region susceptible to this disease prevalence using a time series model. The accurate locations of 4437 CL diagnosed from 2011 to 2015 were obtained to be used in the time series model. Temperature, number of days with temperature over 30 °C, and number of earthquake were related to CL incidence using the Seasonal Auto-correlated Integrated Moving Average (SARIMA) model according to the Box-Jenkins method. In addition, the relationship between land use and surface soil type in 500- and 1000-m radius around the CL patients were investigated. The SARIMA models showed significant associations between environmental and meteorological variables and CL incidence adjusted for seasonality and auto-correlation. The result indicated that there are need more robust preventive programs in earthquake-prone areas with high temperature and inceptisol soil type than other areas. In addition, the region with these characteristics should be considered as high-risk areas for CL prevalence.

Trends in the Mechanistic and Dynamic Modeling of Infectious Diseases

The dynamics of infectious disease epidemics are driven by interactions between individuals with differing disease status (e.g., susceptible, infected, immune). Mechanistic models that capture the dynamics of such “dependent happenings” are a fundamental tool of infectious disease epidemiology. Recent methodological advances combined with access to new data sources and computational power have resulted in an explosion in the use of dynamic models in the analysis of emerging and established infectious diseases. Increasing use of models to inform practical public health decision making has challenged the field to develop new methods to exploit available data and appropriately characterize the uncertainty in the results. Here, we discuss recent advances and areas of active research in the mechanistic and dynamic modeling of infectious disease. We highlight how a growing emphasis on data and inference, novel forecasting methods, and increasing access to “big data” are changing the field of infectious disease dynamics. We showcase the application of these methods in phylodynamic research, which combines mechanistic models with rich sources of molecular data to tie genetic data to population-level disease dynamics. As dynamics and mechanistic modeling methods mature and are increasingly tied to principled statistical approaches, the historic separation between the infectious disease dynamics and “traditional” epidemiologic methods is beginning to erode; this presents new opportunities for cross pollination between fields and novel applications.

Satellite Hyperspectral Imagery to Support Tick-Borne Infectious Diseases Surveillance

This study proposed the use of satellite hyperspectral imagery to support tick-borne infectious diseases surveillance based on monitoring the variation in amplifier hosts food sources. To verify this strategy, we used the data of the human rickettsiosis occurrences in southeastern Brazil, region in which the emergence of this disease is associated with the rising capybara population. Spatio-temporal analysis based on Monte Carlo simulations was used to identify risk areas of human rickettsiosis and hyperspectral moderate-resolution imagery was used to identify the increment and expansion of sugarcane crops, main food source of capybaras. In general, a pixel abundance associated with increment of sugarcane crops was detected in risk areas of human rickettsiosis. Thus, the hypothesis that there is a spatio-temporal relationship between the occurrence of human rickettsiosis and the sugarcane crops increment was verified. Therefore, due to the difficulty of monitoring locally the distribution of infectious agents, vectors and animal host's, satellite hyperspectral imagery can be used as a complementary tool for the surveillance of tick-borne infectious diseases and potentially of other vector-borne diseases.