Proof of concept of a method that assesses the spread of microbial infections with spatially explicit and non-spatially explicit data

<p>Abstract</p> <p>Background</p> <p>A method that assesses bacterial spatial dissemination was explored. It measures microbial genotypes (defined by electrophoretic patterns or EP), host, location (farm), interfarm Euclidean distance, and time. Its proof of concept (construct and internal validity) was evaluated using a dataset that included 113 <it>Staphylococcus aureus </it>EPs from 1126 bovine milk isolates collected on 23 farms between 1988 and 2005.</p> <p>Results</p> <p>Construct validity was assessed by comparing results based on the interfarm Euclidean distance (a spatially explicit measure) and those produced by the (non-spatial) interfarm number of isolates reporting the same EP. The distance associated with EP spread correlated with the interfarm number of isolates/EP (<it>r </it>= .59, <it>P </it>< 0.02). Internal validity was estimated by comparing results obtained with different versions of the same indices. Concordance was observed between: (a) EP distance (estimated microbial dispersal over space) and EP speed (distance/year, <it>r </it>= .72, <it>P </it>< 0.01), and (b) the interfarm number of isolates/EP (when measured on the basis of non-repeated cow testing) and the same measure as expressed by repeated testing of the same animals (<it>r </it>= .87, <it>P </it>< 0.01). Three EPs (2.6% of all EPs) appeared to be super-spreaders: they were found in 26.75% of all isolates. Various indices differentiated local from spatially disseminated infections and, within the local type, infections suspected to be farm-related were distinguished from cow-related ones.</p> <p>Conclusion</p> <p>Findings supported both construct and internal validity. Because 3 EPs explained 12 times more isolates than expected and at least twice as many isolates as other EPs did, false negative results associated with the remaining EPs (those erroneously identified as lacking spatial dispersal when, in fact, they disseminated spatially), if they occurred, seemed to have negligible effects. Spatial analysis of laboratory data may support disease surveillance systems by generating hypotheses on microbial dispersal ability.</p

Optimization of Epidemiologic Interventions: Evaluation of Spatial and Non-Spatial Methods That Identify Johne’s Disease-Infected Subpopulations Targeted for Intervention

The potential costs and/or benefits associated with two epidemiological methods were compared. Using the same epidemiologic dataset (74 Israeli dairy herds tested for bovine paratuberculosis of which 57 farms were regarded to be infected, and 619 non-tested herds), the efficacy associated with the identification of the target population where control or preventive measures could be applied was evaluated by: 1) A method that applied geographical information systems (GIS), spatial statistics, network analysis (infective spatial links or ISL); and 2) A method that only partially applied spatial techniques. Based on the herd size of tested and non-tested farms, the geographical area of influence of each infected farm was estimated. Using the Euclidean distance between tested farms (distances between 2701 farm pairs), the ISL method calculated two measures of spatial connectivity: the number of links/farm and the ISL index. These measures are analogous to the number of roads connecting a city (links/farm) and the width of a road (index). The more links and/or the greater the average index ( width ), the greater the chances of an infected farm to disseminate an infection (especially to neighboring farms). While not reaching statistical significance, positive indices of Moran\u27s I test for some spatial lags prompted the additional investigation of a subset of 547 farm pairs. This subset included 33 farm pairs (16 individual farms) which displayed \u3e 2 links/farm, and ISL indices \u3e7.5 times greater than average (high ISL farms). Regarding as cost the number of infected cows selected to receive an intervention, and as benefit the number of susceptible cows within the area of influence of an infected farm, hypothetical interventions implemented on the 16 high ISL farms yielded 39 % greater benefits and occupied a territory 9.5% smaller than decisions based on the 16 farms showing the highest prevalence. The analysis on spatial infective connectivity may lead to earlier, farm-specific and more beneficial, decisions than methods based only on outcomes (later data), such as prevalence

Testing-related and geo-demographic indicators strongly predict COVID-19 deaths in the United States during March of 2020

The COVID-19 pandemic has wreaked havoc around the globe and caused significant disruptions across multiple domains. Moreover, different countries have been differentially impacted by COVID-19 — a phenomenon that is due to a multitude of complex and often interacting determinants. Understanding such complexity and interacting factors requires both compelling theory and appropriate data analytic techniques. Regarding data analysis, one question that arises is how to analyze extremely non-normal data, such as those variables evidencing L-shaped distributions. A second question concerns the appropriate selection of a predictive modelling technique when the predictors derive from multiple domains (e.g., testing-related variables, population density), and both main effects and interactions are examined.https://www.journals.elsevier.com/biomedical-and-environmental-scienceshj2022Veterinary Tropical Disease

Epidemic protection zones : centred on cases or based on connectivity?

When an exotic infectious disease invades a susceptible environment, protection zones are enforced. Historically, such zones have been shaped as circles of equal radius (ER), centred on the location of infected premises. Because the ER policy seems to assume that epidemic dissemination is driven by a similar number of secondary cases generated per primary case, it does not consider whether local features, such as connectivity, influence epidemic dispersal. Here we explored the efficacy of ER protection zones. By generating a geographically explicit scenario that mimicked an actual epidemic, we created protection zones of different geometry, comparing the cost-benefit estimates of ER protection zones to a set of alternatives, which considered a pre-existing connecting network (CN) – the road network. The hypothesis of similar number of cases per ER circle was not substantiated: the number of units at risk per circle differed up to four times among ER circles. Findings also showed that even a small area (of <115 km2) revealed network properties. Because the CN policy required 20% less area to be protected than the ER policy, and the CN-based protection zone included a 23.8% greater density of units at risk/km2 than the ER-based alternative, findings supported the view that protection zones are likely to be less costly and more effective if they consider connecting structures, such as road, railroad and/or river networks. The analysis of local geographical factors (contacts, vectors and connectivity) may optimize the efficacy of control measures against epidemics.http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1865-1682ab2012ab2013 (Author correction

Development of disease-specific, context-specific surveillance models : avian influenza (H5N1)-related risks and behaviours in African countries

Avian influenza virus (H5N1) is a rapidly disseminating infection that affects poultry and, potentially, humans. Because the avian virus has already adapted to several mammalian species, decreasing the rate of avian-mammalian contacts is critical to diminish the chances of a total adaptation of H5N1 to humans. To prevent the pandemic such adaptation could facilitate, a biology-specific disease surveillance model is needed, which should also consider geographical and socio-cultural factors. Here we conceptualized a surveillance model meant to capture H5N1-related biological and cultural aspects, which included food processing, trade, and cooking-related practices, as well as incentives (or disincentives) for desirable behaviours. This proof-of-concept was tested with data collected from 378 Egyptian and Nigerian sites (local [backyard] producers/ live bird markets /village abattoirs/ commercial abattoirs and veterinary agencies). Findings revealed numerous opportunities for pathogens to disseminate, as well as lack of incentives to adopt preventive measures, and factors that promoted epidemic dissemination. Supporting such observations, the estimated risk for H5N1-related human mortality was higher than previously reported. The need for multi-dimensional disease surveillance models, which may detect risks at higher levels than models that only measure one factor or outcome, was supported. To develop efficient surveillance systems, interactions should be captured, which include but exceed biological factors. This low-cost and easily implementable model, if conducted over time, may identify focal instances where tailored policies may diminish both endemicity and the total adaptation of H5N1 to the human species.http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1863-23782017-02-28hb2016Food ScienceProduction Animal Studie

Connecting network properties of rapidly disseminating epizoonotics

BACKGROUND: To effectively control the geographical dissemination of infectious diseases, their properties need to be determined. To test that rapid microbial dispersal requires not only susceptible hosts but also a pre-existing, connecting network, we explored constructs meant to reveal the network properties associated with disease spread, which included the road structure. METHODS: Using geo-temporal data collected from epizoonotics in which all hosts were susceptible (mammals infected by Foot-and-mouth disease virus, Uruguay, 2001; birds infected by Avian Influenza virus H5N1, Nigeria, 2006), two models were compared: 1) ‘connectivity’, a model that integrated bio-physical concepts (the agent’s transmission cycle, road topology) into indicators designed to measure networks (‘nodes’ or infected sites with short- and long-range links), and 2) ‘contacts’, which focused on infected individuals but did not assess connectivity. RESULTS: The connectivity model showed five network properties: 1) spatial aggregation of cases (disease clusters), 2) links among similar ‘nodes’ (assortativity), 3) simultaneous activation of similar nodes (synchronicity), 4) disease flows moving from highly to poorly connected nodes (directionality), and 5) a few nodes accounting for most cases (a ‘‘20:800 pattern). In both epizoonotics, 1) not all primary cases were connected but at least one primary case was connected, 2) highly connected, small areas (nodes) accounted for most cases, 3) several classes of nodes were distinguished, and 4) the contact model, which assumed all primary cases were identical, captured half the number of cases identified by the connectivity model. When assessed together, the synchronicity and directionality properties explained when and where an infectious disease spreads. CONCLUSIONS: Geo-temporal constructs of Network Theory’s nodes and links were retrospectively validated in rapidly disseminating infectious diseases. They distinguished classes of cases, nodes, and networks, generating information usable to revise theory and optimize control measures. Prospective studies that consider pre-outbreak predictors, such as connecting networks, are recommended.The National Veterinary Research Institute, Vom, Plateau, Nigeria; the Center for Non-Linear Studies of Los Alamos National Laboratory; and partially funded by Defense Threat Reduction Agency (DTRA) Grant CBT-09-IST-05-1-0092 (to JMF).http://www.plosone.orgab2012ab2013 (Author correction

Assessing the dynamics and complexity of disease pathogenicity using 4-dimensional immunological data

Investigating disease pathogenesis and personalized prognostics are major biomedical needs. Because patients sharing the same diagnosis can experience different outcomes, such as survival or death, physicians need new personalized tools, including those that rapidly differentiate several inflammatory phases. To address these topics, a pattern recognition-based method (PRM) that follows an inverse problem approach was designed to assess, in <10min, eight concepts: synergy, pleiotropy, complexity, dynamics, ambiguity, circularity, personalized outcomes, and explanatory prognostics (pathogenesis). By creating thousands of secondary combinations derived from blood leukocyte data, the PRM measures synergic, pleiotropic, complex and dynamic data interactions, which provide personalized prognostics while some undesirable features—such as false results and the ambiguity associated with data circularity-are prevented. Here, this method is compared to Principal Component Analysis (PCA) and evaluated with data collected from hantavirus-infected humans and birds that appeared to be healthy. When human data were examined, the PRM predicted 96.9 % of all surviving patients while PCA did not distinguish outcomes. Demonstrating applications in personalized prognosis, eight PRM data structures sufficed to identify all but one of the survivors. Dynamic data patterns also distinguished survivors from non-survivors, as well as one subset of non-survivors, which exhibited chronic inflammation. When the PRM explored avian data, it differentiated immune profiles consistent with no, early, or late inflammation. Yet, PCA did not recognize patterns in avian data. Findings support the notion that immune responses, while variable, are rather deterministic: a low number of complex and dynamic data combinations may be enough to, rapidly, unmask conditions that are neither directly observable nor reliably forecasted.Conacyt of Mexico (Consejo Nacional de Ciencia y Tecnologíahttp://www.frontiersin.org/Immunologyam2020Veterinary Tropical Disease

Connecting Network Properties of Rapidly Disseminating Epizoonotics

To effectively control the geographical dissemination of infectious diseases, their properties need to be determined. To test that rapid microbial dispersal requires not only susceptible hosts but also a pre-existing, connecting network, we explored constructs meant to reveal the network properties associated with disease spread, which included the road structure.Using geo-temporal data collected from epizoonotics in which all hosts were susceptible (mammals infected by Foot-and-mouth disease virus, Uruguay, 2001; birds infected by Avian Influenza virus H5N1, Nigeria, 2006), two models were compared: 1) 'connectivity', a model that integrated bio-physical concepts (the agent's transmission cycle, road topology) into indicators designed to measure networks ('nodes' or infected sites with short- and long-range links), and 2) 'contacts', which focused on infected individuals but did not assess connectivity.THE CONNECTIVITY MODEL SHOWED FIVE NETWORK PROPERTIES: 1) spatial aggregation of cases (disease clusters), 2) links among similar 'nodes' (assortativity), 3) simultaneous activation of similar nodes (synchronicity), 4) disease flows moving from highly to poorly connected nodes (directionality), and 5) a few nodes accounting for most cases (a "20:80" pattern). In both epizoonotics, 1) not all primary cases were connected but at least one primary case was connected, 2) highly connected, small areas (nodes) accounted for most cases, 3) several classes of nodes were distinguished, and 4) the contact model, which assumed all primary cases were identical, captured half the number of cases identified by the connectivity model. When assessed together, the synchronicity and directionality properties explained when and where an infectious disease spreads.Geo-temporal constructs of Network Theory's nodes and links were retrospectively validated in rapidly disseminating infectious diseases. They distinguished classes of cases, nodes, and networks, generating information usable to revise theory and optimize control measures. Prospective studies that consider pre-outbreak predictors, such as connecting networks, are recommended

Geo-temporal patterns to design cost-effective interventions for zoonotic diseases -the case of brucellosis in the country of Georgia

IntroductionControl of zoonosis can benefit from geo-referenced procedures. Focusing on brucellosis, here the ability of two methods to distinguish disease dissemination patterns and promote cost-effective interventions was compared.MethodGeographical data on bovine, ovine and human brucellosis reported in the country of Georgia between 2014 and 2019 were investigated with (i) the Hot Spot (HS) analysis and (ii) a bio-geographical (BG) alternative.ResultsMore than one fourth of all sites reported cases affecting two or more species. While ruminant cases displayed different patterns over time, most human cases described similar geo-temporal features, which were associated with the route used by migrant shepherds. Other human cases showed heterogeneous patterns. The BG approach identified small areas with a case density twice as high as the HS method. The BG method also identified, in 2018, a 2.6–2.99 higher case density in zoonotic (human and non-human) sites than in non-zoonotic sites (which only reported cases affecting a single species) –a finding that, if corroborated, could support cost-effective policy-making.DiscussionThree dissemination hypotheses were supported by the data: (i) human cases induced by sheep-related contacts; (ii) human cases probably mediated by contaminated milk or meat; and (iii) cattle and sheep that infected one another. This proof-of-concept provided a preliminary validation for a method that may support cost-effective interventions oriented to control zoonoses. To expand these findings, additional studies on zoonosis-related decision-making are recommended

Molecular biomarkers in the context of focal therapy for prostate cancer: Recommendations of a delphi consensus from the focal therapy society

BACKGROUND: Focal therapy (FT) for prostate cancer (PCa) is promising. However, long-term oncological results are awaited and there is no consensus on follow-up strategies. Molecular biomarkers (MB) may be useful in selecting, treating and following up men undergoing FT, though there is limited evidence in this field to guide practice. We aimed to conduct a consensus meeting, endorsed by the Focal Therapy Society, amongst a large group of experts, to understand the potential utility of MB in FT for localized PCa. METHODS: A 38-item questionnaire was built following a literature search. The authors then performed three rounds of a Delphi Consensus using DelphiManager, using the GRADE grid scoring system, followed by a face-to-face expert meeting. Three areas of interest were identified and covered concerning MB for FT, 1) the current/present role; 2) the potential/future role; 3) the recommended features for future studies. Consensus was defined using a 70% agreement threshold. RESULTS: Of 95 invited experts, 42 (44.2%) completed the three Delphi rounds. Twenty-four items reached a consensus and they were then approved at the meeting involving (N.=15) experts. Fourteen items reached a consensus on uncertainty, or they did not reach a consensus. They were re-discussed, resulting in a consensus (N.=3), a consensus on a partial agreement (N.=1), and a consensus on uncertainty (N.=10). A final list of statements were derived from the approved and discussed items, with the addition of three generated statements, to provide guidance regarding MB in the context of FT for localized PCa. Research efforts in this field should be considered a priority. CONCLUSIONS: The present study detailed an initial consensus on the use of MB in FT for PCa. This is until evidence becomes available on the subject