COVID-19 related interdisciplinary methods: Preventing errors and detecting research opportunities.

More than 130,000 peer-reviewed studies have been published within one year after COVID-19 emerged in many countries. This large and rapidly growing field may overwhelm the synthesizing abilities of both researchers and policy-makers. To provide a sinopsis, prevent errors, and detect cognitive gaps that may require interdisciplinary research methods, the literature on COVID-19 is summarized, twice. The overall purpose of this study is to generate a dialogue meant to explain the genesis of and/or find remedies for omissions and contradictions. The first review starts in Biology and ends in Policy. Policy is chosen as a destination because it is the setting where cognitive integration must occur. The second review follows the opposite path: it begins with stated policies on COVID-19 and then their assumptions and disciplinary relationships are identified. The purpose of this interdisciplinary method on methods is to yield a relational and explanatory view of the field -one strategy likely to be incomplete but usable when large bodies of literature need to be rapidly summarized. These reviews identify nine inter-related problems, research needs, or omissions, namely: (1) nation-wide, geo-referenced, epidemiological data collection systems (open to and monitored by the public); (2) metrics meant to detect non-symptomatic cases -e.g., test positivity-; (3) cost-benefit oriented methods, which should demonstrate they detect silent viral spreaders even with limited testing; (4) new personalized tests that inform on biological functions and disease correlates, such as cell-mediated immunity, co-morbidities, and immuno-suppression; (5) factors that influence vaccine effectiveness; (6) economic predictions that consider the long-term consequences likely to follow epidemics that growth exponentially; (7) the errors induced by self-limiting and/or implausible paradigms, such as binary and reductionist approaches; (8) new governance models that emphasize problem-solving skills, social participation, and the use of scientific knowledge; and (9) new educational programs that utilize visual aids and audience-specific communication strategies. The analysis indicates that, to optimally address these problems, disciplinary and social integration is needed. By asking what is/are the potential cause(s) and consequence(s) of each issue, this methodology generates visualizations that reveal possible relationships as well as omissions and contradictions. While inherently limited in scope and likely to become obsolete, these shortcomings are avoided when this 'method on methods' is frequently practiced. Open-ended, inter-/trans-disciplinary perspectives and broad social participation may help researchers and citizens to construct, de-construct, and re-construct COVID-19 related research.

Biologically grounded scientific methods: The challenges ahead for combating epidemics.

The protracted COVID 19 pandemic may indicate failures of scientific methodologies. Hoping to facilitate the evaluation and/or update of methods relevant in Biomedicine, several aspects of scientific processes are here explored. First, the background is reviewed. In particular, eight topics are analyzed: (i) the history of Higher Education models in reference to the pursuit of science and the type of student cognition pursued, (ii) whether explanatory or actionable knowledge is emphasized depending on the well- or ill-defined nature of problems, (iii) the role of complexity and dynamics, (iv) how differences between Biology and other fields influence methodologies, (v) whether theory, hypotheses or data drive scientific research, (vi) whether Biology is reducible to one or a few factors, (vii) the fact that data, to become actionable knowledge, require structuring, and (viii) the need of inter-/trans-disciplinary knowledge integration. To illustrate how these topics interact, a second section describes four temporal stages of scientific methods: conceptualization, operationalization, validation and evaluation. They refer to the transition from abstract (non-measurable) concepts (such as 'health') to the selection of concrete (measurable) operations (such as 'quantification of ́anti-virus specific antibody titers'). Conceptualization is the process that selects concepts worth investigating, which continues as operationalization when data-producing variables viewed to reflect critical features of the concepts are chosen. Because the operations selected are not necessarily valid, informative, and may fail to solve problems, validations and evaluations are critical stages, which require inter/trans-disciplinary knowledge integration. It is suggested that data structuring can substantially improve scientific methodologies applicable in Biology, provided that other aspects here mentioned are also considered. The creation of independent bodies meant to evaluate biologically oriented scientific methods is recommended.

Decoding Immuno-Competence: A Novel Analysis of Complete Blood Cell Count Data in COVID-19 Outcomes.

BACKGROUND: While 'immuno-competence' is a well-known term, it lacks an operational definition. To address this omission, this study explored whether the temporal and structured data of the complete blood cell count (CBC) can rapidly estimate immuno-competence. To this end, one or more ratios that included data on all monocytes, lymphocytes and neutrophils were investigated. MATERIALS AND METHODS: Longitudinal CBC data collected from 101 COVID-19 patients (291 observations) were analyzed. Dynamics were estimated with several approaches, which included non-structured (the classic CBC format) and structured data. Structured data were assessed as complex ratios that capture multicellular interactions among leukocytes. In comparing survivors with non-survivors, the hypothesis that immuno-competence may exhibit feedback-like (oscillatory or cyclic) responses was tested. RESULTS: While non-structured data did not distinguish survivors from non-survivors, structured data revealed immunological and statistical differences between outcomes: while survivors exhibited oscillatory data patterns, non-survivors did not. In survivors, many variables (including IL-6, hemoglobin and several complex indicators) showed values above or below the levels observed on day 1 of the hospitalization period, displaying L-shaped data distributions (positive kurtosis). In contrast, non-survivors did not exhibit kurtosis. Three immunologically defined data subsets included only survivors. Because information was based on visual patterns generated in real time, this method can, potentially, provide information rapidly. DISCUSSION: The hypothesis that immuno-competence expresses feedback-like loops when immunological data are structured was not rejected. This function seemed to be impaired in immuno-suppressed individuals. While this method rapidly informs, it is only a guide that, to be confirmed, requires additional tests. Despite this limitation, the fact that three protective (survival-associated) immunological data subsets were observed since day 1 supports many clinical decisions, including the early and personalized prognosis and identification of targets that immunomodulatory therapies could pursue. Because it extracts more information from the same data, structured data may replace the century-old format of the CBC.

Data structuring may prevent ambiguity and improve personalized medical prognosis.

Topics expected to influence personalized medicine (PM), where medical decisions, practices, and treatments are tailored to the individual patient, are reviewed. Lack of discrimination due to different biological conditions that express similar values of numerical variables (ambiguity) is regarded to be a major potential barrier for PM. This material explores possible causes and sources of ambiguity and offers suggestions for mitigating the impacts of uncertainties. Three causes of ambiguity are identified: (1) delayed adoption of innovations, (2) inadequate emphases, and (3) inadequate processes used when new medical practices are developed and validated. One example of the first problem is the relative lack of medical research on "compositional data" -the type that characterizes leukocyte data. This omission results in erroneous use of data abundantly utilized in medicine, such as the blood cell differential. Emphasis on data output ‒not biomedical interpretation that facilitates the use of clinical data‒ exemplifies the second type of problems. Reliance on tools generated in other fields (but not validated within biomedical contexts) describes the last limitation. Because reductionism is associated with these problems, non-reductionist alternatives are reviewed as potential remedies. Data structuring (converting data into information) is considered a key element that may promote PM. To illustrate a process that includes data-information-knowledge and decision-making, previously published data on COVID-19 are utilized. It is suggested that ambiguity may be prevented or ameliorated. Provided that validations are grounded on biomedical knowledge, approaches that describe certain criteria - such as non-overlapping data intervals of patients that experience different outcomes, immunologically interpretable data, and distinct graphic patterns - can inform, at personalized bases, earlier and/or with fewer observations.

From numbers to medical knowledge: harnessing combinatorial data patterns to predict COVID-19 resource needs and distinguish patient subsets.

Background: The COVID-19 pandemic intensified the use of scarce resources, including extracorporeal membrane oxygenation (ECMO) and mechanical ventilation (MV). The combinatorial features of the immune system may be considered to estimate such needs and facilitate continuous open-ended knowledge discovery. Materials and methods: Computer-generated distinct data patterns derived from 283 white blood cell counts collected within five days after hospitalization from 97 COVID-19 patients were used to predict patient's use of hospital resources. Results: Alone, data on separate cell types-such as neutrophils-did not identify patients that required MV/ECMO. However, when structured as multicellular indicators, distinct data patterns displayed by such markers separated patients later needing or not needing MV/ECMO. Patients that eventually required MV/ECMO also revealed increased percentages of neutrophils and decreased percentages of lymphocytes on admission. Discussion/conclusion: Future use of limited hospital resources may be predicted when combinations of available blood leukocyte-related data are analyzed. New methods could also identify, upon admission, a subset of COVID-19 patients that reveal inflammation. Presented by individuals not previously exposed to MV/ECMO, this inflammation differs from the well-described inflammation induced after exposure to such resources. If shown to be reproducible in other clinical syndromes and populations, it is suggested that the analysis of immunological combinations may inform more and/or uncover novel information even in the absence of pre-established questions.

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

Introduction: Control 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. Method: Geographical 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. Results: More 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. Discussion: Three 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.

Multi-Cellular Immunological Interactions Associated With COVID-19 Infections.

To rapidly prognosticate and generate hypotheses on pathogenesis, leukocyte multi-cellularity was evaluated in SARS-CoV-2 infected patients treated in India or the United States (152 individuals, 384 temporal observations). Within hospital (<90-day) death or discharge were retrospectively predicted based on the admission complete blood cell counts (CBC). Two methods were applied: (i) a "reductionist" one, which analyzes each cell type separately, and (ii) a "non-reductionist" method, which estimates multi-cellularity. The second approach uses a proprietary software package that detects distinct data patterns generated by complex and hypothetical indicators and reveals each data pattern's immunological content and associated outcome(s). In the Indian population, the analysis of isolated cell types did not separate survivors from non-survivors. In contrast, multi-cellular data patterns differentiated six groups of patients, including, in two groups, 95.5% of all survivors. Some data structures revealed one data point-wide line of observations, which informed at a personalized level and identified 97.8% of all non-survivors. Discovery was also fostered: some non-survivors were characterized by low monocyte/lymphocyte ratio levels. When both populations were analyzed with the non-reductionist method, they displayed results that suggested survivors and non-survivors differed immunologically as early as hospitalization day 1.

Early network properties of the COVID-19 pandemic - The Chinese scenario.

OBJECTIVES: To control epidemics, sites more affected by mortality should be identified. METHODS: Defining epidemic nodes as areas that included both most fatalities per time unit and connections, such as highways, geo-temporal Chinese data on the COVID-19 epidemic were investigated with linear, logarithmic, power, growth, exponential, and logistic regression models. A z-test compared the slopes observed. RESULTS: Twenty provinces suspected to act as epidemic nodes were empirically investigated. Five provinces displayed synchronicity, long-distance connections, directionality and assortativity - network properties that helped discriminate epidemic nodes. The rank I node included most fatalities and was activated first. Fewer deaths were reported, later, by rank II and III nodes, while the data from rank I-III nodes exhibited slopes, the data from the remaining provinces did not. The power curve was the best fitting model for all slopes. Because all pairs (rank I vs. rank II, rank I vs. rank III, and rank II vs. rank III) of epidemic nodes differed statistically, rank I-III epidemic nodes were geo-temporally and statistically distinguishable. CONCLUSIONS: The geo-temporal progression of epidemics seems to be highly structured. Epidemic network properties can distinguish regions that differ in mortality. This real-time geo-referenced analysis can inform both decision-makers and clinicians.

Where and when to vaccinate? Interdisciplinary design and evaluation of the 2018 Tanzanian anti-rabies campaign.

OBJECTIVES: Hoping to improve health-related effectiveness, a two-phase vaccination against rabies was designed and executed in northern Tanzania in 2018, which included geo-epidemiological and economic perspectives. METHODS: Considering the local bio-geography and attempting to rapidly establish a protective ring around a city at risk, the first phase intervened on sites surrounding that city, where the population density was lower than in the city at risk. The second phase vaccinated a rural area. RESULTS: No rabies-related case has been reported in the vaccinated areas for over a year post-immunisation; hence, the campaign is viewed as highly cost-effective. Other metrics included: rapid implementation (concluded in half the time spent on other campaigns) and the estimated cost per protected life, which was 3.28 times lower than in similar vaccinations. CONCLUSIONS: The adopted design emphasised local bio-geographical dynamics: it prevented the occurrence of an epidemic in a city with a higher demographic density than its surrounding area and it also achieved greater effectiveness than average interventions. These interdisciplinary, policy-oriented experiences have broad and immediate applications in settings of limited and/or time-sensitive (expertise, personnel, and time available to intervene) resources and conditions.

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 <10 min, 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.

Ecological and Epidemiological Findings Associated with Zoonotic Rabies Outbreaks and Control in Moshi, Tanzania, 2017-2018.

Approximately 1500 people die annually due to rabies in the United Republic of Tanzania. Moshi, in the Kilimanjaro Region, reported sporadic cases of human rabies between 2017 and 2018. In response and following a One Health approach, we implemented surveillance, monitoring, as well as a mass vaccinations of domestic pets concurrently in >150 villages, achieving a 74.5% vaccination coverage (n = 29, 885 dogs and cats) by September 2018. As of April 2019, no single human or animal case has been recorded. We have observed a disparity between awareness and knowledge levels of community members on rabies epidemiology. Self-adherence to protective rabies vaccination in animals was poor due to the challenges of costs and distances to vaccination centers, among others. Incidence of dog bites was high and only a fraction (65%) of dog bite victims (humans) received post-exposure prophylaxis. A high proportion of unvaccinated dogs and cats and the relative intense interactions with wild dog species at interfaces were the risk factors for seropositivity to rabies virus infection in dogs. A percentage of the previously vaccinated dogs remained unimmunized and some unvaccinated dogs were seropositive. Evidence of community engagement and multi-coordinated implementation of One Health in Moshi serves as an example of best practice in tackling zoonotic diseases using multi-level government efforts. The district-level establishment of the One Health rapid response team (OHRRT), implementation of a carefully structured routine vaccination campaign, improved health education, and the implementation of barriers between domestic animals and wildlife at the interfaces are necessary to reduce the burden of rabies in Moshi and communities with similar profiles.

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

BACKGROUND: 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 Staphylococcus aureus EPs from 1126 bovine milk isolates collected on 23 farms between 1988 and 2005. RESULTS: 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 (r = .59, P < 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, r = .72, P < 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 (r = .87, P < 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. CONCLUSION: 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.

Receiver operating characteristic-based assessment of a serological test used to detect Johne's disease in Israeli dairy herds.

The overall accuracy of an enzyme-linked immunosorbent assay (ELISA) used to detect Johne's disease at herd level was explored in relation to an imperfect test (fecal culture) in 57 Israeli dairy herds. Receiver-operating characteristic (ROC) analysis indicated an area under the curve (AUC) that corresponded to a test accuracy of 82.0% (69.5% to 90.9%; 95% confidence), with optimized herd sensitivity and herd specificity of 70.4% and 83.3%, respectively; and predictive values of 79.2 (+) and 75.8% (-). The optimal ELISA cutoff was 3.16% (> 3.16% seropositive cows in a herd), which was associated with likelihood ratios (LR) of 4.22 (+LR) and 0.36 (-LR), and post-test probabilities of 0.79 (+) and 0.17 (-). For herds with < or = 200 cows (n = 19 herds), the 95% confidence interval (CI) for the AUC was 0.62-0.97 and the optimal cutoff was 3.33% (HSe = 87.5, HSp = 81.8); for herds with > 200 but < or = 270 cows (n = 19 herds), the 95% AUC CI was 0.62-0.97 and the optimal cutoff was 1.13% (HSe = 90.0, HSp = 77.78); and for herds with > 270 cows (n = 19 herds), the 95% AUC CI was 0.69-0.99 and the optimal cutoff was 0.7% (HSe = 100.0, HSp = 70.0). The AUC was not influenced by across-herd prevalence [R2 (adjusted) = 0.0, P > 0.05]. Findings may be applied to facilitate targeted sampling of herds similar to those evaluated. For instance, a test cutoff of 0.76% could be considered for "ruling disease in," while a cutoff of 3.7% could be used for "ruling disease out." Caveats that may influence this analysis are discussed.

Multifactorial relationships between intramammary invasion by Staphylococcus aureus and bovine leukocyte markers.

We explored the hypothesis that the outcome of bacterial invasion (infection or no infection) may depend on immunologic factors when bacterial and environmental factors are kept constant. Leukocyte surface molecules (CD3, CD2, CD4, CD8, CD11b, and CD45r) were assessed before and 3 times after intramammary infusion of Staphylococcus aureus in 5 dairy cows. The somatic cell count (SCC/mL), bacterial count (colony-forming units [CFUs]/mL), ratio of milk phagocytes (mononuclear [Mphi] plus polymorphonuclear [PMN] cells) to lymphocytes (P/L index), and ratio of PMN to Mphi cells (PMN/Mphi index) were determined. Although all cows showed evidence of inflammation resulting from the infusion (the median P/L ratio was 11 times greater 1 d after infusion than before infusion), bacteria were not obtained from the milk of 2 cows. Threshold-like responses, resulting in bacterial counts that approached zero (indicating no infection) and SCCs of less than 500000/mL, were observed when the milk CD2+ lymphocyte proportion exceeded 73% (P < or = 0.007). At 1 d after infusion, 7 immune factors distinguished infected cows from those without infection with more than 95% confidence: compared with infected cows, uninfected cows had higher proportions of CD3+, CD2+, CD4+, and CD8+ T cells, higher densities of CD3 and CD2 molecules per cell, and a higher density of CD11b molecules on milk Mphi cells. At 7 d after infusion, the PMN/Mphi index was lower (94% confidence) in uninfected than in infected cows. At 14 d, the CD2, CD8, and CD45r marker densities were lower than those at 1 d (P < 0.02), findings compatible with memory function. Synergism was suggested by the combined effects of the proportions of CD3+, CD2+, and CD11b+ cells, which explained 75.5% of the bacterial-count variability (P < 0.001); alone, none of these markers predicted CFU variability. These results support further studies aimed at identifying cows capable (or incapable) of early bacterial clearance.

Nature and Consequences of Biological Reductionism for the Immunological Study of Infectious Diseases.

Evolution has conserved "economic" systems that perform many functions, faster or better, with less. For example, three to five leukocyte types protect from thousands of pathogens. To achieve so much with so little, biological systems combine their limited elements, creating complex structures. Yet, the prevalent research paradigm is reductionist. Focusing on infectious diseases, reductionist and non-reductionist views are here described. The literature indicates that reductionism is associated with information loss and errors, while non-reductionist operations can extract more information from the same data. When designed to capture one-to-many/many-to-one interactions-including the use of arrows that connect pairs of consecutive observations-non-reductionist (spatial-temporal) constructs eliminate data variability from all dimensions, except along one line, while arrows describe the directionality of temporal changes that occur along the line. To validate the patterns detected by non-reductionist operations, reductionist procedures are needed. Integrated (non-reductionist and reductionist) methods can (i) distinguish data subsets that differ immunologically and statistically; (ii) differentiate false-negative from -positive errors; (iii) discriminate disease stages; (iv) capture in vivo, multilevel interactions that consider the patient, the microbe, and antibiotic-mediated responses; and (v) assess dynamics. Integrated methods provide repeatable and biologically interpretable information.

Relationships between the phagocytic ability of milk macrophages and polymorphonuclear cells and somatic cell counts in uninfected cows.

Phagocyte numbers and activities were compared in milk from 2 groups of uninfected mammary-gland quarters from 3 cows each: 6 quarters with a high (> or = 200 000/mL) somatic cell concentration (SCC), analyzed as 4 individual quarters and 1 pooled sample; and 12 quarters with a low SCC (< 200 000/mL), analyzed as 6 paired samples. The concentrations and ability of macrophages and polymorphonuclear (PMN) cells to phagocytize fluorescent microspheres were determined by flow cytometry after exposure of the cells to the microspheres. The macrophages and PMNs contained 2 major subpopulations, characterized by low phagocytic (LP) or high phagocytic (HP) ability. The quarters with high SCCs had significantly lower percentages of HP cells than did the quarters with low SCCs (P < 0.01). Whether mammary-gland quarters or cows were the unit of analysis, the HP/LP ratio was negatively related to the SCC (P < 0.04), which explained more than 50% of the SCC variability. Thus, poor bovine mammary-gland phagocytic ability may be associated with high SCC. Longitudinal studies are suggested to further explore and characterize these relationships.

Identification of case clusters and counties with high infective connectivity in the 2001 epidemic of foot-and-mouth disease in Uruguay.

OBJECTIVE: To evaluate the influence of individual spatial units (ie, counties) on the epidemic spread of foot-and-mouth disease (FMD) virus. SAMPLE POPULATION: 163 counties in Uruguay where there was an outbreak of FMD between April 23 and July 11, 2001. PROCEDURE: A geographically referenced database was created, and the distance between counties (13,203 county pairs), road density of counties (163 counties), and time when cases were reported in those counties (11 weeks of the epidemic) were considered to assess global spatial and spatial-temporal autocorrelation, determine the contribution of links connecting pairs of counties with infected animals, and allow us to hypothesize the influence for spread during the epidemic for counties with greater than the mean infective link contributions. RESULTS: Case clusters were indicated by the Moran Iand Mantel tests during the first 6 weeks of the epidemic. Spatial lags between pairs of counties with infected animals revealed case clustering before and after vaccination was implemented. Temporal lags predicted autocorrelation for up to 3 weeks. Link indices identified counties expected to facilitate epidemic spread. If control measures had been implemented in counties with a high index link (identifiable as early as week 1 of the epidemic), they could have prevented (by week 11 of the epidemic) at least 2.5 times as many cases per square kilometer than the same measures implemented in counties with average link indices. CONCLUSIONS AND CLINICAL RELEVANCE: Analysis of spatial autocorrelation and infective link indices may identify network conditions that facilitate (or prevent) disease spread.