Socio-economic, epidemiological and geographic features based on GIS-integrated mapping to identify malarial hotspots

Managing vector-borne diseases in a geoAI-enabled society. Malaria as an example

More than 17 % of all infectious diseases are caused by vector-borne diseases resulting in more than 1 billion cases and over 1 million deaths each year. Of these malaria continues to be a global burden in over eighty countries. As societies become more digitalised, the availability of geospatially enabled health and disease information will become more abundant. With this, the ability to assess health and disease risks in real-time will become a reality. The purpose of this study was to examine how geographic information, geospatial technologies and spatial data science are being used to reduce the burden of vector-borne diseases such as malaria and explore the opportunities that lie ahead with GeoAI and other geospatial technology advancements. Malaria is a dynamic and complex system and as such a range of data and approaches are needed to tackle different parts of the malaria cycle at different local and global scales. Geospatial technologies provide an integrated framework vital for monitoring, analysing and managing vector-borne diseases. GeoAI and technological advancements are useful for enhancing real-time assessments, accelerating the decision making process and spatial targeting of interventions. Training is needed to enhance the use of geospatial information for the management of vector-borne diseases.

Evaluation of Vulnerability Status of the Infection Risk to COVID-19 Using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): A Case Study of Addis Ababa City, Ethiopia

COVID-19 is a disease caused by a new coronavirus called SARS-CoV-2 and is an accidental global public health threat. Because of this, WHO declared the COVID-19 outbreak a pandemic. The pandemic is spreading unprecedently in Addis Ababa, which results in extraordinary logistical and management challenges in response to the novel coronavirus in the city. Thus, management strategies and resource allocation need to be vulnerability-oriented. Though various studies have been carried out on COVID-19, only a few studies have been conducted on vulnerability from a geospatial/location-based perspective but at a wider spatial resolution. This puts the results of those studies under question while their findings are projected to the finer spatial resolution. To overcome such problems, the integration of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) has been developed as a framework to evaluate and map the susceptibility status of the infection risk to COVID-19. To achieve the objective of the study, data like land use, population density, and distance from roads, hospitals, bus stations, the bank, markets, COVID-19 cases, health care units, and government offices are used. The weighted overlay method was used; to evaluate and map the susceptibility status of the infection risk to COVID-19. The result revealed that out of the total study area, 32.62% (169.91 km2) falls under the low vulnerable category (1), and the area covering 40.9% (213.04 km2) under the moderate vulnerable class (2) for infection risk of COVID-19. The highly vulnerable category (3) covers an area of 25.31% (132.85 km2), and the remaining 1.17% (6.12 km2) is under an extremely high vulnerable class (4). Thus, these priority areas could address pandemic control mechanisms like disinfection regularly. Health sector professionals, local authorities, the scientific community, and the general public will benefit from the study as a tool to better understand pandemic transmission centers and identify areas where more protective measures and response actions are needed at a finer spatial resolution.

Inequality in geographical distribution of hospitals and hospital beds in densely populated metropolitan cities of Iran

Background

This study aims to assess geographical distribution of hospitals and extent of inequalities in hospital beds against socioeconomic status (SES) of residents of five metropolitan cities in Iran.

Methods

A cross-sectional analysis was conducted to measure geographical inequality in hospital and hospital bed distributions of 68 districts in five metropolitan cities during 2016 using geographic information system (GIS), and Gini and Concentration indices. Correlation analysis was performed to show the relationship between the SES and inequality in hospital beds densities.

Results

The study uncovered marked inequalities in hospitals and hospital beds distributions. The Gini indices for hospital beds were greater than 0.55. The aggregated concentration indices for public and private hospital beds were 0.33 and 0.49, respectively. The GIS revealed that 216 (70.6%) hospitals were located in two highest socioeconomic status classes in the cities. Only 29 (9.5%) hospitals were located in the lowest class. The public, private, and the cumulative hospitals beds distributions in Tehran and Esfahan showed significant (p < 0.05) positive correlation with SES of the residents.

Conclusions

The high inequalities in hospital and hospital beds distributions in our study imply an overlooked but growing concern for geographical access to healthcare in rapidly urbanizing metropolitan cities in Iran. Thus, regardless of ownership, decision-makers should emphasize the disadvantaged areas in metropolitan cities when need arises for the establishment of new healthcare facilities in order to ensure fairness in healthcare. The metropolitan cities and rapid urbanization settings in other countries could learn lessons to reduce or prevent similar issues which might have hampered access to healthcare.

Malaria Hotspots: Is There Epidemiological Evidence for Fine-Scale Spatial Targeting of Interventions?

As data at progressively granular spatial scales become available, the temptation is to target interventions to areas with higher malaria transmission - so-called hotspots - with the aim of reducing transmission in the wider community. This paper reviews literature to determine if hotspots are an intrinsic feature of malaria epidemiology and whether current evidence supports hotspot-targeted interventions. Hotspots are a consistent feature of malaria transmission at all endemicities. The smallest spatial unit capable of supporting transmission is the household, where peri-domestic transmission occurs. Whilst the value of focusing interventions to high-burden areas is evident, there is currently limited evidence that local-scale hotspots fuel transmission. As boundaries are often uncertain, there is no conclusive evidence that hotspot-targeted interventions accelerate malaria elimination.

Passive case detection of malaria in Ratanakiri Province (Cambodia) to detect villages at higher risk for malaria

BACKGROUND

Cambodia reduced malaria incidence by more than 75% between 2000 and 2015, a target of the Millennium Development Goal 6. The Cambodian Government aims to eliminate all forms of malaria by 2025. The country's malaria incidence is highly variable at provincial level, but less is known at village level. This study used passive case detection (PCD) data at village level in Ratanakiri Province from 2010 to 2014 to describe incidence trends and identify high-risk areas of malaria to be primarily targeted towards malaria elimination.

METHODS

In 2010, the Cambodian malaria programme created a Malaria Information System (MIS) to capture malaria information at village level through PCD by village malaria workers and health facilities. The MIS data of Ratanakiri Province 2010-2014 were used to calculate annual incidence rates by Plasmodium species at province and commune levels. For estimating the trend at provincial level only villages reporting each year were selected. The communal incidences and the number of cases per village were visualized on a map per Plasmodium species and per year. Analysis of spatial clustering of village malaria cases by Plasmodium species was performed by year.

RESULTS

Overall, malaria annual incidence rates per 1000 inhabitants decreased from 86 (2010) to 30 (2014). Falciparum incidence decreased (by 79% in 2014 compared to 2010; CI 95% 76-82%) more rapidly than vivax incidence (by 19% in 2014 compared to 2010; CI 95% 5-32%). There were ten to 16 significant spatial clusters each year. Big clusters tended to extend along the Cambodian-Vietnamese border and along the Sesan River. Three clusters appeared throughout all years (2010-2014): one with 21 villages appeared each year, the second shrunk progressively from 2012 to 2014 and the third was split into two smaller clusters in 2013 and 2014.

CONCLUSION

The decline of malaria burden can be attributed to intensive malaria control activities implemented in the areas: distribution of a long-lasting insecticidal net per person and early diagnosis and prompt treatment. Dihydro-artemisinin piperaquine was the only first-line treatment for all malaria cases. No radical treatment with primaquine was provided for Plasmodium vivax cases, which could explain the slow decrease of P. vivax due to relapses. To achieve malaria elimination by 2025, priority should be given to the control of stable malaria clusters appearing over time.

Malaria control and prevention towards elimination: data from an eleven-year surveillance in Shandong Province, China

BACKGROUND

Shandong Province experienced a declining malaria trend of local-acquired transmission, but the increasing imported malaria remains a challenge. Therefore, understanding the epidemiological characteristics of malaria and the control and elimination strategy and interventions is needed for better planning to achieve the overall elimination goal in Shandong Province.

METHODS

A retrospective study was conducted and all individual cases from a web-based reporting system were reviewed and analysed to explore malaria-endemic characteristics in Shandong from 2005 to 2015. Annual malaria incidence reported in 2005-2015 were geo-coded and matched to the county-level. Spatial cluster analysis was performed to evaluate any identified spatial disease clusters for statistical significance. The space-time cluster was detected with high rates through the retrospective space-time analysis scanning using the discrete Poisson model.

RESULTS

The overall malaria incidence decreased to a low level during 2005-2015. In total, 1564 confirmed malaria cases were reported, 27.1% of which (n = 424) were indigenous cases. Most of the indigenous case (n = 339, 80.0%) occurred from June to October. However, the number and scale of imported cases have been increased but no significant difference was observed during months. Shandong is endemic for both Plasmodium vivax (n = 730) and Plasmodium falciparum (n = 674). The disease is mainly distributed in Southern (n = 710) and Eastern region (n = 424) of Shandong, such as Jinning (n = 214 [13.7%]), Weihai (n = 151 [9.7%]), and Yantai (n = 107 [6.8%]). Furthermore, the spatial cluster analysis of malaria cases from 2005 to 2015 indicated that the diseased was not randomly distributed. For indigenous cases, a total of 15 and 2 high-risk counties were determined from 2005 to 2009 (control phase) and from 2010 to 2015 (elimination phase), respectively. For imported cases, a total of 26 and 29 high-risk counties were determined from 2005 to 2009 (control phase) and from 2010 to 2015 (elimination phase), respectively. The method of spatial scan statistics identified different 13 significant spatial clusters between 2005 and 2015. The space-time clustering analysis determined that the most likely cluster included 14 and 19 counties for indigenous and imported, respectively.

CONCLUSIONS

In order to cope with the requirements of malaria elimination phase, the surveillance system should be strengthened particularity on the frequent migration regions as well as the effective multisectoral cooperation and coordination mechanisms. Specific response packages should be tailored among different types of cities and capacity building should also be improved mainly focus on the emergence response and case management. Fund guarantees for scientific research should be maintained both during the elimination and post-elimination phase to consolidate the achievements of malaria elimination.

Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda

Background

Rural populations experience several barriers to accessing clinical facilities for malaria diagnosis. Increasing penetration of ICT and mobile-phones and subsequent m-Health applications can contribute overcoming such obstacles.

Methods

GIS is used to evaluate the feasibility of m-Health technologies as part of anti-malaria strategies. This study investigates where in Uganda: (1) malaria affects the largest number of people; (2) the application of m-Health protocol based on the mobile network has the highest potential impact.

Results

About 75% of the population affected by Plasmodium falciparum malaria have scarce access to healthcare facilities. The introduction of m-Health technologies should be based on the 2G protocol, as 3G mobile network coverage is still limited. The western border and the central-Southeast are the regions where m-Health could reach the largest percentage of the remote population. Six districts (Arua, Apac, Lira, Kamuli, Iganga, and Mubende) could have the largest benefit because they account for about 28% of the remote population affected by falciparum malaria with access to the 2G mobile network.

Conclusions

The application of m-Health technologies could improve access to medical services for distant populations. Affordable remote malaria diagnosis could help to decongest health facilities, reducing costs and contagion. The combination of m-Health and GIS could provide real-time and geo-localized data transmission, improving anti-malarial strategies in Uganda. Scalability to other countries and diseases looks promising.

Ethnobotanical perspective of antimalarial plants: traditional knowledge based study

BACKGROUND

Considering the demand of antimalarial plants it has become essential to find and locate them for their optimal extraction. The work aims to find plants with antimalarial activities which were used by the local people; to raise the value of traditional knowledge system (TKS) prevalent in the study region; to compile characteristics of local plants used in malaria treatment (referred as antimalarial plants) and to have its spatial distribution analysis to establish a concept of geographical health.

METHODS

Antimalarial plants are listed based on literature survey and field data collected during rainy season, from 85 respondents comprised of different ethnic groups. Ethno-medicinal utilities of plants was extracted; botanical name, family, local name, part used, folklore, geographical location and image of plants were recorded after cross validating with existing literatures. The interview was trifurcated in field, Vaidya/Hakims and house to house. Graphical analysis was done for major plants families, plant part used, response of people and patients and folklore. Mathematical analysis was done for interviewee's response, methods of plant identification and people's preferences of TKS through three plant indices.

RESULTS

Fifty-one plants belonging to 27 families were reported with its geographical attributes. It is found plant root (31.75 %) is used mostly for malaria treatment and administration mode is decoction (41.2 %) mainly. The study area has dominance of plants of family Fabaceae (7), Asteraceae (4), Acanthaceae (4) and Amaranthaceae (4). Most popular plants found are Adhatoda vasica, Cassia fistula and Swertia chirata while  % usage of TKS is 82.0 % for malaria cure.

CONCLUSION

The research findings can be used by both scientific community and common rural people for bio-discovery of these natural resources sustainably. The former can extract the tables to obtain a suitable plant towards finding a suitable lead molecule in a drug discovery project; while the latter can meet their local demands of malaria, scientifically.