Abstract
Research typically promotes two types of outcomes (inventions and discoveries), which induce a virtuous cycle: something suspected or desired (not previously demonstrated) may become known or feasible once a new tool or procedure is invented and, later, the use of this invention may discover new knowledge. Research also promotes the opposite sequence—from new knowledge to new inventions. This bidirectional process is observed in geo-referenced epidemiology—a field that relates to but may also differ from spatial epidemiology. Geo-epidemiology encompasses several theories and technologies that promote inter/transdisciplinary knowledge integration, education, and research in population health. Based on visual examples derived from geo-referenced studies on epidemics and epizootics, this report demonstrates that this field may extract more (geographically related) information than simple spatial analyses, which then supports more effective and/or less costly interventions. Actual (not simulated) bio-geo-temporal interactions (never captured before the emergence of technologies that analyze geo-referenced data, such as geographical information systems) can now address research questions that relate to several fields, such as Network Theory. Thus, a new opportunity arises before us, which exceeds research: it also demands knowledge integration across disciplines as well as novel educational programs which, to be biomedically and socially justified, should demonstrate cost-effectiveness. Grounded on many bio-temporal-georeferenced examples, this report reviews the literature that supports this hypothesis: novel educational programs that focus on geo-referenced epidemic data may help generate cost-effective policies that prevent or control disease dissemination.
Introduction
Geo-epidemiology may be described as an inter-disciplinary field that, based on geo-referenced and bio-dynamic data, attempts to prevent disease dissemination. Hoping to clarify the similarities and/or differences between geo-epidemiology and other related fields (1), here the literature on visualizations associated with disease dispersal is reviewed. Such an exercise is meant to emphasize that geo-epidemiology may promote earlier, cost–benefit oriented, geography- and time-specific epidemiologic interventions.
This review also describes considerations associated with technological development and education—in particular, how to teach novel interdisciplinary and decision-making oriented programs. They refer to knowledge validation which, in turn, is associated with knowledge integration (2).
The driving motivation for this report is that disease dispersal affects everybody, everywhere. As illustrated by COVID-19, avian influenza, and cholera (among many diseases), unless prevented, epidemics and enzootics may seriously affect humans and non-humans (3–5).
From inventions to knowledge creation
While research promotes technological development, the opposite is also observed. For example, the emergence of geographical information systems (GIS) has fostered public health (6). GIS tools have been integral to infectious disease surveillance, vaccination campaign planning, and optimizing responses to public health crises such as COVID-19. These efforts have laid the groundwork for integrating spatial and temporal analyses more effectively.
Geo-epidemiology vs. spatial epidemiology
While closely associated, space and geography are not synonymous. Space seems to be the larger category while geography is just one sub-domain. Yet, geography tends to be the richer concept because its contents and contexts are not always found in non-geographic space.
While geography refers to the study of the Earth (an actual, not a hypothetical entity), space refers to the study of any (actual or hypothetical) surface, located anywhere. Because our planet is composed of many non-randomly distributed elements (e.g., rivers, mountains, cities, forests, farms, and roads traveled by human and non-human individuals), geo-epidemiology differs substantially from the study of space, which could be imagined as a static environment—where there are no seasons and is inhabited by a homogeneously distributed population (6). Hence, diseases may be better understood when geo-referenced and temporal data are analyzed.
Accordingly, geo-epidemiology informs on relationships involving populations, rivers, forests, lakes, mountains, roads and many other geo-referenced entities, which are dynamic and may exist before diseases occur. Because geographical variables are non-hypothetical, they can be measured directly and because bio-geographical relationships change over time, the analysis of disease dispersal requires multidimensional analyses (7, 8).
While any tool used in spatial analysis, in principle, can also be used in geo-epidemiology, the opposite is not necessarily possible: patterns detected when geographical data are analyzed may be absent in (or missed by) spatial models. While spatial analysis is prone to uni-disciplinary/specialized (reductionist) approaches (8), geo-epidemiology is inherently inter/transdisciplinary and non-reductionist (9).
This report emphasizes infectious diseases that disseminate temporally and geographically, i.e., epidemics and epizootics. Such diseases usually utilize pre-existing connecting structures. To prevent or control them, cost/benefit-oriented analyses are necessary. Given the apparent lack of academic programs on geo-epidemiology, new educational (inter/transdisciplinary) programs seem needed.
Inter/transdisciplinary knowledge relates to but also differs from multidisciplinary knowledge. That is so because multi-disciplinarity does not necessarily integrate knowledge generated in several disciplines. For instance, the work conducted by electricians, plumbers, and carpenters in the process of rebuilding a house does not require previous integration of their expertise: following pre-established instructions, they could just apply what they previously learned. In contrast, interdisciplinary projects require the production of a novel solution that fits a specific (and usually novel) problem (9). When a substantial amount of new knowledge needs to be created to solve a specific problem, the term trans-disciplinarity tends to be used (10).
Because inter/trans-disciplinary knowledge cannot be communicated with a language grounded on any specialized field, new languages and templates may be required. A language common to many fields and constituencies may be facilitated when potential users share the same interest, context or field of application. Participatory approaches may promote the creation of such languages (11, 12).
Consequently, a process that identifies invalid, obsolete, and/or fragmented knowledge may foster problem solving (13). Non-reductionist, data-driven analysis of visually explicit information (such as geo-referenced data on disease dispersal) may promote inter/trans-disciplinary knowledge integration and prevent invalid inferences (14, 15).
The design of this study
This material describes both the way diseases are investigated bio-geo-temporally, and how inter−/transdisciplinary educational and research processes can be promoted. Three sections describe: (i) features and/or properties of geo-epidemiology; (ii) decision-making and applications (in particular, those based on cost/benefit-related considerations), and (iii) concepts associated with education and research methods—especially those grounded on visual data.
Section I. Features and properties of geo-epidemiology
Pre-established, geographically explicit connectivity may inform earlier
The ability to measure unambiguous connecting structures that predate disease emergence is a feature that distinguishes geo-epidemiology from simple spatial approaches. While connectors are associated with contacts, they also differ from one another (16). While contacts are human or non-human individuals, connectors refer to physical structures individuals utilize while traveling and/or contacting one another, e.g., a tunnel, road, bridge, airplane, etc. Unlike approaches that focus on contacts, methods that describe connectivity do not need to identify individuals—identifying the locations of connectors (which precede the occurrence of infections) may suffice. While the contact-oriented approach depends on highly variable data (which may be available only after a crucial event has occurred), connectivity-oriented approaches only need to collect data on pre-existing connectors and, consequently, they provide information inherently prognostic.
Because geo-epidemiology reflects how diseases disseminate, disease dispersal is necessarily based on pre-established connecting networks. Roads, rivers, railroads are examples of pre-established connectors. Only by using connecting structures that predate the emergence (or re-emergence) of a pathogen, can a disease spread out.
Therefore, some earlier concepts (such as the time and location of the first or ‘index’ case) are not necessarily valid because—when cases are reported in places where more than one connecting structures exist—, epidemic or enzootic processes tend to occur (16). Figure 1A illustrates this concept: by plotting actual data on disease dispersal in relation to connecting structures (e.g., the highway network), it is observed that the case regarded as the first (‘index’) case was not well connected–only one of the first 6 cases was located near to or on a highway intersection (a ‘node’ that facilitates two or more dissemination routes, Figure 1A). In contrast, the only case located on the connecting structure explained the subsequent disease dispersal: at days 4–6, the centroid of all epidemic nodes moved into a highly connected dissemination structure (Figure 1B). Thus, to explore disease dispersal not only geographical information is required (on the road network in this case) but also data on the estimated transmission cycle of the pathogen—up to 3 days, in this case.
Figure 1
Network theory-related properties
The distinction between pre-existing connectivity and contacts matters in decision-making. When interventions are designed, it may be difficult to identify the specific contact that could link a specific case with a susceptible individual. In contrast, the specific (geo-referenced) connecting structure (i.e., the ‘node’ that, if blocked, could prevent disease spread) may be easily identified. However, such an identification requires distinguishing ‘average’ from ‘highly connected’ epidemic node-related cases (16–18).
Hence, rapidly elucidating the most likely connecting link that promotes disease dispersal is critical for planning and delivering effective interventions. The underlying principle is that, when the connecting structure associated with a specific disease outbreak is presumptively identified, it is then possible to conceive targeted responses, which are likely to be more effective, more rapidly implemented and/or less costly than non-specific and/or static ones (16).
To take advantage of such a possibility, the study of Network Theory seems required. Applied to epidemiology, Network Theory can be described by several properties, including: (i) Pareto’s 20:80 distribution, (ii) synchronicity, and (iii) directionality. These properties have been empirically observed in three epidemic processes that affected bovine, avian, and human species, respectively (16, 19).
Pareto’s ‘20:80’ distribution refers to the fact that not all epidemic nodes equally influence disease dispersal: only a minority (~20%) of the earlier cases generates most (~80%) of the later cases (20). Consequently, not all epidemic nodes are epidemiologically identical. Because some epidemic nodes are more influential than others, they should be distinguished.
Time- and geography-specific differentiation of epidemic nodes can be objectively determined: they are the sites that include both a connecting structure (e.g., a highway intersection) and the highest percentage of cases at a specific bio-temporal (disease transmission-related) cycle. Operational definitions of what epidemic nodes are and how their influence can be distinguished can be made for a specific disease and environment (Figure 2).
Figure 2
For example, the analysis of geographical interactions—such as the relationships among road density, case density, relative length of roads per area unit—may identify ‘hubs’ or ‘nodes’ of relationships that, if identified before epidemics occur, could lead to anticipatory measures. Such approaches could lead to global anticipatory mapping of all such potential ‘facilitators’ of disease dispersal (21).
Data on geo-bio-temporal interactions may re-evaluate previous theories
Because geo-referenced variables interact with one another, they help re-evaluate earlier theories. One example is disease prevalence, which is now shown to be neither geographically homogeneous nor static (22). For example, expressed as the prevalence of resistance against parasiticides, major differences are observed within the same region in the number of units (farms) that present simple, double or triple resistance (Figure 3).
Figure 3
Similarly, the intra-farm prevalence of bovine Mycobacterium paratuberculosis may differ up to 80% across farms—a finding associated with infective (epidemic) links (23). This means that measuring disease prevalence may be non-informative unless a specific (geo-referenced) region is identified within a specific timeframe.
Section II. Applications: toward informative and cost–benefit related decision-making
Error prevention and extraction of new information
Aggregate data may induce errors. Because such data do not convey relationships, non-aggregate, point-based, high-resolution data are needed to investigate epidemics (24, 25).
The anticipatory creation of geo-referenced datasets that include relationships can facilitate cost-effective interventions (26, 27). For example, such datasets may include information of farm density, animal density, and road networks. In epidemics, analyses of such data can capture a much higher number of expected cases than alternatives (16).
Because they can capture more dimensions than classic approaches, bio-geo-dynamic assessments are likely to prevent errors. For example, apparent gaps in the data (which suggested no new cases occurred several times in the first 70 epidemic days) seemed to occur when time was measured with chronological units (days, Figure 4A). Such patterns were not detected when the same data were reported as generation intervals (Figure 4B). When, instead of reporting hours or days, time was measured together with biological concepts (e.g., when the transmission cycle of the pathogen was considered), the previous gaps were no longer observed (Figure 4B). In addition, the distance between a specific case and the nearest connecting structure can also be captured (17). This geo-bio-temporal metric shows that the number of epidemic cases–expressed as proportion of all cases–, was inversely related with the distance between cases and the nearest road (Figure 4C).
Figure 4
Differentiation of infection types
Bio-geo-temporal analyses can also differentiate infections. At least five infecting types can be distinguished, which may prompt different interventions.
For example, the detection of highly disseminating bacterial strains may lead to earlier, bacterial strain-specific interventions (Figure 5). Furthermore, two sub-varieties can be distinguished within the ‘local’ (no geographical spread) bacterial strain type. Based on the Heterogeneity Index (percent of intra-farm isolates that belong to the same bacterial strain), two subtypes can be differentiated: (a) ‘cow problem’ and (b) the ‘farm problem’ subtypes. A ‘farm problem’ is suspected when most bacterial strains found in a farm belong to the same strain but have not been found elsewhere (e.g., the percentage of isolates that belong to the same strain is higher than 50%, Figure 6). When the percentage of isolates that belong to the same strain is lower than 50% (when a large diversity of bacterial strains is found in the same farm, but they do not show spatial dispersal), a ‘cow problem’ is suspected (Figure 6).
Figure 5
Figure 6
If these analyses were frequently conducted, they could facilitate earlier (cost-effective) decisions. In one investigated case, decisions could have been made 5 years earlier, which could have prevented between 6 and 14 percentage points of disease occurrence (28).
More effective and/or less costly decisions: continuity vs. contiguity
Bio-geo-temporal inferences based on continuous relationships can improve the validity and benefits of decision-making (30, 31). For example, twice as many cases can be detected per unit of area when connectivity is considered (Figure 7A) than when the local connectivity is ignored (Figure 7B).
Figure 7
More effective and/or less costly decisions: the ‘sandwich’ approach
Bio-geo-temporal analysis can detect multi-dimensional, complex relationships. For example, when the number of Foot-and-Mouth Disease (FMD) cases was classified according to four descriptors (farm size, animal density, county-specific percentage of dairy farms, and county road density [length of roads/county area]), a higher proportion of FMD cases were reported in areas characterized by (i) small and medium size land parcels, (ii) higher animal density, (iii) >20% farms specialized in dairy production, and (iv) high road density (Figure 8). By intersecting and linking together these classes, a higher proportion of cases can be found within a smaller proportion of the area to be controlled (32).
Figure 8
More effective and/or less costly decisions: enhanced detection of secondary cases
While classic approaches emphasize only one or a couple of disciplinary perspectives, geo-epidemiology integrates all disciplines relevant to the study of disease dispersal and offers a visually explicit validation. For example, the hypothesis that all cases have equal influence on disease dispersal can be tested against the hypothesis that highly linked epidemic nodes have more influence on disease dispersal than poorly linked nodes. When tested with two procedures that create circles of identical area (one grounded on Network Theory, the other based on ‘near neighbor’ contacts), the Network alternative captured a much longer and less fragmented connecting structure than the contact alternative (Figures 9A–D).
Figure 9
Least costly, more effective detection of clusters of any geometric shape
‘Disease clusters’ have been defined as ‘hot spots’ that escape clear statistical or geometric definitions (33). Assumptions associated with ‘disease clusters’ include: (i) the view that disease dispersal is equally influenced by every primary case; (ii) future secondary cases (susceptible individuals) are always close to primary cases, so circles centered on the location of primary cases should capture secondary cases; and (iii) control circles of the same radius can apply to any epidemic, regardless of the infecting pathogen, affected species, geographical location and/or season. Following these assumptions, control circles of 3-km radius, centered on the location of an infected farm (‘primary’ case), have been imposed in European pig, poultry and bovine farms affected by different pathogens, at different times (34–36).
Yet, such policies can miss non-circular disease clusters (37). In contrast, Figure 10 shows that bio-geo-temporal assessments can estimate the benefit/cost ratio of interventions applied to geometrically irregular disease clusters, even in very small and non-circular infected areas (38).
Figure 10
Rapid design and implementation of emergency vaccinations with limited resources
Cost–benefit oriented approaches are especially required when resources are limited and urgencies emerge, such as unexpected vaccinations. One such a situation was experienced in Tanzania, in 2018, when an outbreak of human rabies started close to a major urban center (38).
Then, the adopted strategy first implemented a ‘ring’ vaccination near to but outside a major urban center, which later expanded into the low-density, rural area comprised between the ‘ring’ and Mount Kilimanjaro (Figure 11). By containing the virus within two ‘walls’ (the ring vaccination on one side, the Kilimanjaro on the other), the time involved in implementing this strategy was negligible compared to standard practices. No rabies-related case was reported in the vaccinated area for over a year and the cost of the 2018 Tanzanian campaign was 3.28 times lower than anti-rabies vaccinations implemented in similar environments (38).
Figure 11
Applications in human diseases: test positivity-based, cost-effective interventions
Geo-epidemiology may also apply to human medicine. For example, Chinese geo-referenced and temporal data on COVID-19 have revealed Network properties (19).
Geo-epidemiology may also be instrumental in solving a major problem encountered in many epidemics. That is when many of the infected individuals are asymptomatic. As seen in COVID-19, asymptomatic individuals are major disease disseminators: they are not aware that they are infected and do not request medical assistance (39). Such a situation creates a deceiving consequence: diagnostic tests tend to be conducted among symptomatic, not among asymptomatic individuals.
This situation induces high percentages of test positivity (TP or percentage of tested individuals that yield ‘positive’ tests), even when ‘positive’ individuals are less likely to disseminate the disease than asymptomatic ones. Consequently, high TP percentages do not necessarily reflect the true status of the population but the status of those that seek testing.
The alternative to testing 100% of the population on a given day—usually, an unfeasible goal—is to test as much as possible, so low percentages of the TP are found in many areas and only one (or very few) area(s) display high TP percentages. When such a situation is found, the prompt removal (isolation) of positive cases located in the central area may prevent disease dissemination into neighboring areas. Geo-epidemiology could implement such a strategy (39).
To reduce disease dispersal at the lowest cost and/or in the shortest period, a double approach could consider (i) county-level, temporal and geo-referenced data on test positivity.
(TP), and (ii) cost–benefit related considerations (39). This strategy could focus not on spending resources equally and constantly across all areas but, instead, it could briefly concentrate resources in a small area where the TP is substantially higher than the surrounding area (Figure 12).
Figure 12
Applications in zoonoses
Geo-referenced, cost-effective decisions may prevent zoonoses (40). They can detect more cases in smaller areas than alternatives (Figures 13A–D).
Figure 13
Analyses that integrate bio-geo-temporal data could identify where and when to intervene at lower cost/greater benefit. As shown in Figure 14, when geographical locations that report human and non-human brucellosis cases are considered, zoonotic sites may exhibit a much higher case density and, therefore, should be prioritized in interventions (40, 41).
Figure 14
Section III. Teaching that supports inter/trans-disciplinary research and vice versa
Balancing cost with effectiveness
To ensure feasibility, the implementation of proposed educational programs should also address existing systemic challenges, including resource availability, interdisciplinary collaboration barriers, and the integration of new curricula into established academic frameworks. Leveraging partnerships with organizations experienced in GIS education and public health could accelerate the development of such programs.
Method development: the DIKW (data, information, knowledge, ‘wisdom’) process
To be used, data should be transformed into information, later reformatted as knowledge and, finally, applied. The DIKW (data, information, knowledge, ‘wisdom’) process could move epidemiology from data-based into knowledge-rich inputs that inform decisions (42, 43).
To achieve it, new programs could consider learning-related aspects, such as: (i) pattern recognition, (ii) knowledge creation/interpretation/integration, and (iii) knowledge use (44, 45).
Visual language and interdisciplinary problem-solving
The aspects mentioned above may develop new interdisciplinary language (46). Because visualizations convey information interpretable across disciplines, geo-referenced, visual data may promote learning, research and problem-solving (47–50).
Georeferenced disease datasets that foster research and education
The anticipatory creation of disease-related, bio-geo-temporal datasets may also foster method development and critical thinking. Cognitive skills that foster data analysis are now taught even in secondary schools (51, 52). Educational and research programs on geo-epidemiology may emphasize problem-solving (53–55).
To optimize learning, the dynamic complexities associated with changing epidemic processes should be addressed in the language used in educational practices (56, 57). Because they inform on numerous and dynamic relationships, visually explicit teaching formats seem more appropriate than static alternatives (58). Bio-geographical teaching strategies promote inter-personal skills, critical thinking, and knowledge discovery (59, 60).
Building and teaching how to use geo-epidemiological tools is globally needed (61). Because many health-related graduate programs were created before COVID-19 emerged, adjusting learning environments to pandemic-related learning needs may be necessary (62, 63).
Is global graduate education on geo-epidemiology both needed and feasible?
COVID-19 was and still is a tragic lesson: it revealed major gaps in scientific knowledge (64). Bibliographic searches provide indirect but strong hints on probable omissions: when the keywords ‘geo-referenced’ and ‘COVID’ were searched for, on August 27 of 2024, the Web of Science only retrieved 29 hits. They represented 0.00005% of all the literature on COVID-19 published at that time (29 / 524,284). One likely explanation for such a cognitive gap is the lack of educational programs on geo-epidemiology.
The need for visually explicit, data-driven education on disease dispersal has been reported (65). While traditional teaching cannot be scaled up, online education can (66). Data- driven, online, student-centered education may promote critical thinking as well as validation and lifelong, question-generating skills (67).
New educational programs on geo-epidemiology may be rapidly developed because five conditions or resources are already mature and available: (i) a large, inter-disciplinary group of educators/researchers, (ii) international libraries on disease-related datasets, (iii) a methodology that integrates theory with operations applicable to many diseases affecting human and non-human species, (iv) many research publications that offer numerous examples of cost–benefit oriented interventions, and (v) the ability to develop and use context-specific software.
Based on electronic platforms, new educational programs can be offered at low or negligible costs. Using such formats, geo-epidemiology could provide new interdisciplinary programs, which also capture One Health dimensions (68, 69).
Limitations
Numerous tools and research findings likely to influence geo-epidemiology have not been comprehensively examined here. They include: (i) new sources of geo-referenced disease data (70, 71) and (ii) new algorithms that address combinatorial problems (72–74).
Summary and conclusion
The theoretical foundation, operational consequences, and educational needs associated with geo-epidemiology are summarized. At least two emphases characterize bio-geo-temporal assessments: (1) the analysis of connecting structures established before disease emergence, (2) measures that facilitate site-specific, cost/benefit-related decision-making. It is suggested that new, data-driven, participatory educational and research programs may foster earlier, less costly, and/or more effective interventions against disease dispersal.
Statements
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found at: Links and citations to the papers that reported the original data are provided.
Author contributions
SS: Investigation, Methodology, Writing – original draft, Writing – review & editing. EG: Conceptualization, Supervision, Writing – original draft, Writing – review & editing. AR: Investigation, Visualization, Writing – original draft, Writing – review & editing. FF: Conceptualization, Supervision, Writing – original draft, Writing – review & editing. MK: Supervision, Writing – original draft, Writing – review & editing. LM: Supervision, Writing – original draft, Writing – review & editing. AH: Conceptualization, Writing – original draft, Writing – review & editing. JF: Investigation, Writing – original draft, Writing – review & editing.
Funding
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Acknowledgments
The Archival Data on Brucellosis human and animal cases were collected, processed and analyzed under the US DTRA funded Grant “Preparation of the atlas of zoonotic infections in South Caucasus” (HDTRA11910044).
Conflict of interest
MK was employed by the company KB One Health LLC.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1.
KirbyRSDelmelleEEberthJM. Advances in spatial epidemiology and geographic information systems. Ann Epidemiol. (2017) 27:1–9. doi: 10.1016/j.annepidem.2016.12.001
2.
MohanKJainRRameshB. Knowledge networking to support medical new product development. Decis Support Syst. (2007) 43:1255–73. doi: 10.1016/j.dss.2006.02.005
3.
AarestrupFMBontenMKoopmansM. One health preparedness for the next pandemics. Lancet Regional Health-Europe. (2021) 9:100210. doi: 10.1016/j.lanepe.2021.100210
4.
RamírezIJLeeJ. Deconstructing the spatial effects of El Niño and vulnerability on cholera rates in Peru: wavelet and GIS analyses. Spat Spatiotemporal Epidemiol. (2022) 40:100474. doi: 10.1016/j.sste.2021.100474
5.
Smallman-RaynorMCliffAD. The geographical spread of avian influenza a (H5N1): Panzootic transmission (December 2003–may 2006), pandemic potential, and implications. Annals of the Assoc American Geographers. (2008) 98:553–82. doi: 10.1080/00045600802098958
6.
PerraNGonçalvesB. Modeling and predicting human infectious diseases. Social Phenomena. (2015) 23:59–83. doi: 10.1007/978-3-319-14011-7_4
7.
EdsallRM. Design and usability of an enhanced geographic information system for exploration of multivariate health statistics. Prof Geogr. (2003) 55:146–60. doi: 10.1111/0033-0124.5502003
8.
Diez RouxAV. Investigating neighborhood and area effects on health. AJPH. (2001) 91:1783–9. doi: 10.2105/AJPH.91.11.1783
9.
HittnerJBHoogesteijnALFairJMvan RegenmortelMHRivasAL. The third cognitive revolution: the consequences and possibilities for biomedical research. EMBO Rep. (2019) 20:e47647. doi: 10.15252/embr.201847647
10.
ChoiBCPakAW. Multidisciplinarity, interdisciplinarity, and transdisciplinarity in health research, services, education and policy: 3. Discipline, inter-discipline distance, and selection of discipline. Clin Invest Med. (2008) 31:E41–8. doi: 10.25011/cim.v31i1.3140
11.
ŚliwaK. Languages in problem solving and modeling. JEMI. (2012) 8:69–82. doi: 10.7341/2012845
12.
SilvestreEATorres da SilvaV. Conflict detection among multiple norms in multi-agent systems. Appl Artif Intell. (2018) 32:388–418. doi: 10.1080/08839514.2018.1481591
13.
SieglerRSChenZ. Differentiation and integration: guiding principles for analyzing cognitive change. Dev Sci. (2008) 11:433–48. doi: 10.1111/j.1467-7687.2008.00689.x
14.
RivasALLeitnerGJankowskiMDHoogesteijnALIandiorioMJChatzipanagiotouSet al. Nature and consequences of biological reductionism for the immunological study of infectious diseases. Front Immunol. (2017) 8:612. doi: 10.3389/fimmu.2017.00612
15.
WangMWuBKinshukCNSSpectorJM. Connecting problem-solving and knowledge-construction processes in a visualization-based learning environment. Comput Educ. (2013) 68:293–306. doi: 10.1016/j.compedu.2013.05.004
16.
RivasALFasinaFOHoogesteynALKonahSNFeblesJLPerkinsDJet al. Connecting network properties of rapidly disseminating epizoonotics. PLoS One. (2012) 7:e39778. doi: 10.1371/journal.pone.0039778
17.
RivasALChowellGSchwagerSJFasinaFOHoogesteijnALSmithSDet al. Lessons from Nigeria: the role of roads in the geo-temporal progression of the avian influenza (H5N1). Epidemiol Infect. (2010) 138:192–8. doi: 10.1017/S0950268809990495
18.
RivasALTennenbaumSEAparicioJPHoogesteynALMohammedHCastillo-ChávezCet al. Critical response time (time available to implement effective measures for epidemic control): model building and evaluation. Can J Vet Res. (2003) 67:307–11. PMID:
19.
RivasALFeblesJLSmithSDHoogesteijnALTegosGFasinaFOet al. Early network properties of the COVID-19 pandemic – the Chinese scenario. Int J Infect Dis. (2020) 96:519–23. doi: 10.1016/j.ijid.2020.05.049
20.
AndrianiPMcKelveyB. From Gaussian to Paretian thinking: causes and implications of power laws in organizations. Organ Sci. (2009) 20:1053–71. doi: 10.1287/orsc.1090.0481
21.
HoogesteynALRivasALSmithSDFasinaFOFairJMKosoyM. Assessing complexity and dynamics in epidemics: geographical barriers and facilitators of foot and-mouth disease dissemination. Front Vet Sci. (2023) 10:1149460. doi: 10.3389/fvets.2023.1149460
22.
Rodríguez-VivasRIRivasALChowellGFragosoSHRosarioCRGarcíaZet al. Spatial distribution of acaricide profiles (Boophilus microplus). Vet Parasitol. (2007) 146:158–69. doi: 10.1016/j.vetpar.2007.01.016
23.
RivasALChafferMChowellGEladDOri KorenOSmithSDet al. Optimization of epidemiologic interventions: evaluation of spatial and non-spatial methods that identify Johne’s disease-infected subpopulations targeted to be intervened. Isr J Vet Med. (2008) 63:59–71.
24.
Ortiz-PelaezAPfeifferDUSoares-MagalhãesRJGuitianFJ. Use of social network analysis to characterize the pattern of animal movements in the initial phases of the 2001 foot and mouth disease (FMD) epidemic in the UK. Prev Vet Med. (2006) 76:40–55. doi: 10.1016/j.prevetmed.2006.04.007
25.
JefferyCOzonoffAPaganoM. The effect of spatial aggregation on performance when mapping a risk of disease. Int J Health Geogr. (2014) 13:9. doi: 10.1186/1476-072X-13-9
26.
TatemAJAdamoSBhartiNBurgertCRCastroMDorelienAet al. Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation. Popul Health Metrics. (2012) 10:8. doi: 10.1186/1478-7954-10-8
27.
RuktanonchaiCWNievesJJRuktanonchaiNWNilsenKSteeleJEMatthewsZet al. Estimating uncertainty in geospatial modelling at multiple spatial resolutions: the pattern of delivery via caesarean section in Tanzania. BMJ Glob Health. (2020) 4:e002092. doi: 10.1136/bmjgh-2019-002092
28.
RivasALAndersonKLLymanRSmithSDSchwagerSJ. Proof of concept of a method that assesses the spread of microbial infections with spatially explicit and non-spatially explicit data. Int J Health Geogr. (2008) 7:58. doi: 10.1186/1476-072X-7-58
29.
NandaSKRivasALTrochimWMDeshlerJD. Emphasis on Validation in Research: A Meta-Analysis. Scientometrics. (2000) 48:45–64. doi: 10.1023/A:1005628301541
30.
ChowellGRivasALSmithSDHymanJM. Identification of case clusters and counties with high infective connectivity in the 2001 epidemic of foot-and-mouth disease in Uruguay. Amer J Vet Res. (2006) 67:102–13. doi: 10.2460/ajvr.67.1.102
31.
RivasALKunsbergBChowellGSmithSDHymanJMSchwagerSJ. Human-mediated foot-and-mouth disease epidemic dispersal: disease and vector clusters. J Veterinary Med Ser B. (2006) 53:1–10. doi: 10.1111/j.1439-0450.2006.00904.x
32.
RivasALSchwagerSJSmithSMagriA. Early and cost-effective identification of high risk/priority control areas in foot-and mouth disease epidemics. J Veterinary Med Ser B. (2004) 51:263–71. doi: 10.1111/j.1439-0450.2004.00768.x
33.
McIaffertyS. Disease cluster detection methods: recent developments and public health implications. Ann GIS. (2015) 21:127–33. doi: 10.1080/19475683.2015.1008572
34.
ThulkeHHEisingerDBeerM. The role of movement restrictions and pre-emptive destruction in the emergency control strategy against CSF outbreaks in domestic pigs. Prev Vet Med. (2011) 99:28–37. doi: 10.1016/j.prevetmed.2011.01.002
35.
BackerJAvan RoermundHJFischerEAvan AsseldonkMABergevoetRH. Controlling highly pathogenic avian influenza outbreaks: an epidemiological and economic model analysis. Prev Vet Med. (2015) 121:142–50. doi: 10.1016/j.prevetmed.2015.06.006
36.
RivasALFasinaFOHammondJMSmithSDHoogesteijnALFeblesALet al. Epidemic protection zones: centred on cases or based on connectivity?Transbound Emerg Dis. (2012) 59:464–9. doi: 10.1111/j.1865-1682.2011.01301.x
37.
TangoT. Spatial scan statistics can be dangerous. Stat Methods Med Res. (2021) 30:75–86. doi: 10.1177/0962280220930562
38.
FasinaFOMtui-MalamshaNMahitiGRSalluROleNeselleMRubegwaBet al. Where and when to vaccinate? Interdisciplinary design and evaluation of the 2018 Tanzanian anti-rabies campaign. Int J Infect Dis. (2020) 95:352–60. doi: 10.1016/j.ijid.2020.03.037
39.
RivasALHoogesteijnALHittnerJBvan RegenmortelMHVKempaiahPVogazianosPet al. Toward a COVID-19 testing policy: where and how to test when the purpose is to isolate silent spreaders. medRxiv [Preprint]. (2020) 1–34. doi: 10.1101/2020.12.22.20223651
40.
RivasALSmithSDBasiladzeVChaligavaTMalaniaLBurjanadzeIet al. Geo-temporal patterns to design cost-effective interventions for zoonotic diseases –the case of brucellosis in the country of Georgia. Front Vet Sci. (2023) 10:1270505. doi: 10.3389/fvets.2023.1270505
41.
ThrelfallAGMeahSFischerAJCooksonRRutterHKellyMP. The appraisal of public health interventions: the use of theory. J Public Health (Oxf). (2015) 37:166–71. doi: 10.1093/pubmed/fdu044
42.
RowleyJ. The wisdom hierarchy: representations of the DIKW hierarchy. J Inf Sci. (2007) 33:163–80. doi: 10.1177/0165551506070706
43.
RytkönenMJP. Not all maps are equal: GIS and spatial analysis in epidemiology. Int J Circumpolar Health. (2004) 63:9–24. doi: 10.3402/ijch.v63i1.17642
44.
WindleMLeeHDCherngSTLeskoCRHanrahanCJacksonJWet al. From epidemiologic knowledge to improved health: a vision for translational epidemiology. Am J Epidemiol. (2019) 188:2049–60. doi: 10.1093/aje/kwz085
45.
ZhuangYTWuFChenCPanYH. Challenges and opportunities: from big data to knowledge in AI 2.0. Front Inf Technol Electron Eng. (2017) 18:3–14. doi: 10.1631/FITEE.1601883
46.
BrackenLJOughtonEA. ‘What do you mean?’ The importance of language in developing interdisciplinary research. Trans Inst Br Geogr. (2006) 31:371–82. doi: 10.1111/j.1475-5661.2006.00218.x
47.
PadillaLMCreem-RegehrSHHegartyMStefanucciJK. Decision making with visualizations: a cognitive framework across disciplines. Cogn Ther Res. (2018) 3:29. doi: 10.1186/s41235-018-0120-9
48.
RikerBARickerPRFaggGAHaklayME. Tool, toolmaker, and scientist: case study experiences using GIS in interdisciplinary research. CaGIS. (2020) 47:350–66. doi: 10.1080/15230406.2020.1748113
49.
MeltonJWSaifulJASheinPP. Interdisciplinary STEM program on authentic aerosol science research and students’ systems thinking approach in problem-solving. Int J Sci Educ. (2022) 44:1419–39. doi: 10.1080/09500693.2022.2080886
50.
HsuT-CLiangY-S. Simultaneously improving computational thinking and foreign language learning: interdisciplinary media with plugged and unplugged approaches. J Educ Comput Res. (2021) 59:1184–207. doi: 10.1177/0735633121992480
51.
BerikanBÖzdemirS. Investigating “problem-solving with datasets” as an implementation of computational thinking: a literature review. J Educ Comput Res. (2020) 58:502–34. doi: 10.1177/0735633119845694
52.
MerloATarlingGFujitaTStaarmanJ. What else can be learned when coding? A configurative literature review of learning opportunities through computational thinking. J Educ Comput Res. (2023) 61:901–24. doi: 10.1177/07356331221133822
53.
ParkSLeeILeeJSulS. Advanced information data-interactive learning system effect for creative design project. KSII Trans Internet Info Syst. (2022) 16:2831–45. doi: 10.3837/tiis.2022.08.021
54.
StanciulescuACastronovoFOliverJ. Assessing the impact of visualization media on engagement in an active learning environment. Int J Math Educ Sci Technol. (2022) 55:1150–70. doi: 10.1080/0020739X.2022.2044530
55.
BremerP-TTourassiGBethelWGaitherKPascucciVXuW. Position papers for the ASCR workshop on visualization for scientific discovery, decision-making, and communication. United States: (2022). Technical Report. S. Department of Energy Office of Scientific and Technical Information. doi: 10.2172/1845708
56.
MielkeJDe GeestSZúñigaFBrunkertTZulligLLPfadenhauerLMet al. Understanding dynamic complexity in context—enriching contextual analysis in implementation science from a constructivist perspective. Front Health Serv. (2022) 2:953731. doi: 10.3389/frhs.2022.953731
57.
RaymondIJ. Intentional practice: a common language, approach and set of methods to design, adapt and implement contextualised wellbeing solutions. Front Health Serv. (2023) 3:963029. doi: 10.3389/frhs.2023.963029
58.
RolfesTRothJSchnotzW. Learning the concept of function with dynamic visualizations. Front Psychol. (2020) 11:693. doi: 10.3389/fpsyg.2020.00693
59.
KühlTMünzerS. The moderating role of additional information when learning with animations compared to static pictures. Instr Sci. (2019) 47:659–77. doi: 10.1007/s11251-019-09498-x
60.
HölsgensRWascherEBauerCBollJBundSDankwart-KammounSet al. Transdisciplinary research along the logic of empowerment: perspectives from four urban and regional transformation projects. Sustain For. (2023) 15:4599. doi: 10.3390/su15054599
61.
CoombeLSeverinsenCARobinsonP. Mapping competency frameworks: implications for public health curricula design. ANZJPH. (2022) 46:564–71. doi: 10.1111/1753-6405.13253
62.
HorneyJAHeathA. Undergraduate and graduate public health programs need changes to teach the public health workforce of the future. Dela J Public Health. (2020) 6:24–6. doi: 10.32481/djph.2020.04.008
63.
CoombeL. Interuniversity collaborations: a model for sustainable specialised public health education programmes. Teach High Educ. (2021) 28:1688–705. doi: 10.1080/13562517.2021.1920576
64.
RivasALvan RegenmortelMHV. COVID-19 related interdisciplinary methods: preventing errors and detecting research opportunities. Methods. (2021) 195:3–14. doi: 10.1016/j.ymeth.2021.05.014
65.
CurrieroFCWychgramCRebmanAWCorriganAEKvitAShieldsTet al. The Lyme and Tickborne disease dashboard: a map-based resource to promote public health awareness and research collaboration. PLoS One. (2021) 16:e0260122. doi: 10.1371/journal.pone.0260122
66.
KalantzisMCopeB. After the COVID-19 crisis: Why higher education may (and perhaps should) never be the same. Educ Philos Theory. (2020) 40:51–5.
67.
QuayJ.Education and the certainty of uncertainty. Educ Philos Theory. (2020) 54:717–60.
68.
RamaswamyRChirwaTSalisburyKNcayiyanaJIbisomiLRispelLet al. Developing a field of study in implementation science for the Africa region: the Wits–UNC AIDS implementation science Fogarty D43. Pedagogy in Health Promotion. (2020) 6:46–55. doi: 10.1177/2373379919897088
69.
Mtui-MalamshaNSalluRMahitiGRMohamedHOleNeselleMRubegwaBet al. Ecological and epidemiological findings associated with zoonotic rabies outbreaks and control in Moshi, Tanzania, 2017–2018. Int J Environ Res Public Health. (2019) 16:2816. doi: 10.3390/ijerph16162816
70.
Anon. GitHub - owid/covid-19-data. (2024) 69. Available at: https://github.com/owid/covid-19-data (Accessed October 12, 2024).
71.
Worldometer. COVID - Coronavirus Statistics. (2024). Available at: https://www.worldometers.info/coronavirus/ (Accessed September 15, 2024).
72.
PloskasNAthanasiadisIPapathanasiouJSamarasN. A collaborative spatial decision support system for the capacitated vehicle routing problem on a tabletop display In: LindenILiuSDargamFHernándezJE, editors. Decision support systems IV – Information and knowledge Management in Decision Processes. EWG-DSS EWG-DSS 2014, Lecture Notes in Business Information Processing, vol. 221. Cham: Springer (2015)
73.
DengYKongSAnB. Pretrained cost model for distributed constraint optimization problems. In: Proceedings of the AAAI Conference on Artificial Intelligence (2022) 36:9331–40.
74.
al-HadadBMANadirWHJukilGAM. Intelligent optimization of highway alignments: a novel approach integrating geographic information system and genetic algorithms. Eng Appl Artif Intell. (2024) 133:108037. doi: 10.1016/j.engappai.2024.108037
Summary
Keywords
geo-epidemiology, multidimensional analysis, emergence, geography, epidemics
Citation
Smith SD, Geraghty EM, Rivas AL, Fasina FO, Kosoy M, Malania L, Hoogesteijn AL and Fair JM (2024) Multidimensional perspectives of geo-epidemiology: from interdisciplinary learning and research to cost–benefit oriented decision-making. Front. Public Health 12:1492426. doi: 10.3389/fpubh.2024.1492426
Received
06 September 2024
Accepted
28 November 2024
Published
30 December 2024
Volume
12 - 2024
Edited by
Ana Afonso, NOVA University of Lisbon, Portugal
Reviewed by
Eustachio Cuscianna, University of Bari Aldo Moro, Italy
John DeGroote, University of Northern Iowa, United States
Updates
Copyright
© 2024 Smith, Geraghty, Rivas, Fasina, Kosoy, Malania, Hoogesteijn and Fair.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: A. L. Rivas, alrivas@unm.eduF. O. Fasina, folorunso.fasina@fao.org
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.