R. GARABED, W. JOHNSON, A. PEREZ AND M. THURMOND

The Global control of FMD - Tools, ideas and ideals – Erice, Italy 14-17 October 2008

Appendix 61

FACTORS INFLUENCING GLOBAL FMD REPORTING AND RISK

R. Garabed

1, 2*, W. Johnson3, A. Perez1, 4 and M. Thurmond

1

1Center for Animal Disease Modeling and Surveillance, School of Veterinary Medicine, University of California, One Shields Avenue, Davis, CA 95616, USA

2Present Address: Department of Veterinary Preventive Medicine, The Ohio State University, A100G Sisson Hall, 1920 Coffey Rd, Columbus, OH 43210, USA

3Department of Statistics, Donald Bren School of Information and Computer Sciences, University of California Irvine, Bren Hall 2019, Irvine, CA 92697, USA

4CONICET and Facultad de Ciencias Veterinarias UNR, Ov. Lagos y Ruta 33, Casilda, 2170, Argentina

ABSTRACT

The quality of FMD surveillance and reporting varies globally and over time. Though information about FMD risk varies, harmonious measures are needed for active surveillance programs and development of global disease transmission models. As an alternative to the use of small regional studies and expert opinion estimates, we present models that use available and incomplete data to predict global risk and explore factors related to FMD risk and reporting. Our global models are used to predict true FMD risk and to compare the prediction to reported FMD risk. Maps of the models’ two FMD risk estimates represent differences in perceived FMD risk based on reporting. Traits associated with both FMD reporting and FMD presence varied by geographic region and might provide unconventional targets for intervention. The different prediction model formulae suggest traits of countries and local areas that might contribute to differences in FMD reporting and presence.

1. INTRODUCTION

As is evident in regional FMD situation updates published by FAO EMPRES and EUFMD (2007) and in incidence reports voluntarily submitted to OIE (2008), the quality of FMD surveillance and reporting varies globally and over time. Because information about FMD risk varies, harmonious measures are needed for active surveillance programs. Knowing the number of expected cases in an area is critical for planning surveillance sampling and vaccination. In addition, consistent measures of baseline risk are necessary to develop global transmission models and to measure the progress of control programs. To derive consistent estimates in the face of inconsistent reporting, designers of vaccination and surveillance programs and developers of trade policy have necessarily 1) asked ‘experts’ to make recommendations extrapolated from their knowledge (Wint and Sumption, 2005), 2) had researchers collect data on FMD risk in small regions, or 3) assumed a worst-case scenario (ECHCP, 2007). Though the second technique (collecting data) is the most accurate method, time, expense, international politics, privacy issues, and possible danger to research teams argue against global use of this method. Techniques one and three can be sufficient for trade purposes, but their accuracy may be insufficient for active global surveillance and disease eradication. As an alternative, we present models that use available and incomplete data to provide a standardized approach to predicting global risk. In addition, these models have been used to explore for factors related to FMD risk and reporting.

2. METHODS

The global case-control models were fit using expert opinion, data on FMD presence and absence, and publically available predictor data. The population at risk for FMD in each month in these

The Global control of FMD- Tools, ideas and ideals – Erice, Italy 14-17 October 2008 models was the land area of the world divided into 2500 km2 grid cells. Thus, the “risk of FMD” estimated here was the “probability of at least one case of FMD per cell per month.” The first model controlled for differences in reporting by using 1) OIE-certified FMD-free regions as controls for reported outbreaks and 2) expert opinion about differences in reporting among geographic subregions. This first model provided a best prediction of true FMD risk based on extrapolation from the biological mechanisms behind reported FMD cases and controls. The second model did not control for reporting using method 1, mentioned above, and, instead, used a random subset of all non-case cells as controls. Thus, the second model provided estimates of risk of reported FMD while accounting for general sub-regional differences in reporting. 2.1 The Models The basic form of both models was a case-control Bayesian logistic regression (Formulae 1 and 2) that used expert opinion to adjust for sub-regional differences in reporting and surveillance (Johnson et al., 2009). Experts estimated different reporting and surveillance proportions for the following six sub-regions: 1) Africa, 2) the Americas and Australia, 3) Europe, 4) the Middle East, 5) Central and South Asia, and 6) East and Southeast Asia. Experts also estimated their uncertainty about their assessments. FMDim~ Bernoulli (rim) logit (rim) = log (ρs) + ximβ + zjyθ (1) RFMDim~ Bernoulli (rrim) logit(rrim) = log(ρs) + ximβ + zjyθ (2)

FMD – case-control data on FMD presence (1) or absence (0) for 1998 controls are from OIE certified FMD-free areas matched to the cases by the month and region of occurrence RFMD - case-control data on FMD presence (1) or absence (0) for 1998 controls are matched to the cases based on the month and region of occurrence r – risk: a probability of FMD presence in the given cell for the given month rr – reported risk: a probability of presence of reported FMD in the given cell for the given month ρ – ratio of probability of reporting in cases versus controls x – vector of cell-level predictor data z – vector of country-level predictor data β – vector of regression coefficients for the cell-level predictors (varies by region) θ – vector of regression coefficient for the country-level predictors (varies by region) i – indicator for cell m – indicator for month j – indicator for country (contains a subset of cells) y – indicator for year (contains all months) s – indicator for sub-region (contains a subset of cells and countries) region ≥ sub-region > country ≥ cell

In addition, the predictor variables (listed in section 2.2) and their coefficients, used by the models to predict FMD risk, were selected independently by model, by region, and by year based on the value of the Bayes Factor (Kass and Raftery, 1995) calculated for each candidate model. Four regions (some of which contained multiple sub-regions with differences in reporting) were specified for the purposes of selecting different predictor variables: 1) Africa, 2) the Americas and Australia, 3) Europe and the Middle East, and 4) Asia. Selecting different predictor variables for these regions assumed that the factors influencing FMD occurrence, persistence, and reporting were the same within these regions. 2.2 The Data Cases of FMD voluntarily reported to OIE for the years 1997 and 1998 (FMD BioPortal, 2008) that could be geocoded at the second administrative unit or better based on a reported location name were used to fit both models. For the first, true FMD, model, a subset of cells in areas certified as free-of-FMD by OIE (OIE, 1997-1998) were used as controls. For the second, reported FMD, model, a random subset of non-case cells occurring in the same region and in the same month were used as controls. Data from 1997 were used to construct informed Bayesian priors to fit the models in 1998.

Predictor variables available for the models to use were: Bovine density (FAOSTAT), Buffalo density (FAOSTAT), Small ruminant density (FAOSTAT), Pig density (FAOSTAT), Cell-level water borders (calculated), Cell-level water borders (as a probability, calculated), Human density (ORNL), Bovine meat deficit in the previous year (calculated using ORNL and FAOSTAT), Pig meat deficit in the previous year (calculated using ORNL and FAOSTAT), Sheep and goat meat deficit in the previous

The Global control of FMD - Tools, ideas and ideals – Erice, Italy 14-17 October 2008 year (calculated using ORNL and FAOSTAT), Distance to case this month in the previous year (calculated), Distance to case any time in the preceding year (calculated), Distance to case in the previous month (calculated), Month of Eid ul-Adha, Border length (CIA Factbook), Voice and accountability previous year (Kaufmann et al., 2007), Political stability previous year (Kaufmann et al., 2007), Government effectiveness previous year (Kaufmann et al., 2007), Regulatory quality previous year (Kaufmann et al., 2007), Rule of law previous year (Kaufmann et al., 2007), Control of corruption previous year (Kaufmann et al., 2007), Total literacy rate previous year (CIA Factbook), Female literacy rate previous year (CIA Factbook), Gross domestic product per capita previous year (CIA Factbook), FMD-positive borders proportion previous year (calculated), FMDnot-free borders proportion previous year (calculated), Country reported FMD previous year (OIE, 2008), Country not certified free of FMD previous year (OIE, 1997-1998). A subset of these available predictors was chosen for each region within each model based on a step-wise process that added and subtracted variables from the model if the resulting model improved the Bayes factor by at least ten-fold (Kass and Raftery, 1995). The final models’ predictions were internally validated and found to accurately distinguish between known case and control cells using ROC curves.

3. RESULTS AND DISCUSSION

Maps of the two FMD risk estimates for January 1998 (Figures 1 and 2) represented differences in perceived FMD risk based on reporting. In general, these maps showed that areas of high predicted FMD-risk were more extensive than would be expected based on reported FMD alone. Especially in Africa and Asia, it appears that the conditions for FMD reporting were lacking over nearly half of the area at risk.

Figure 1: Estimated risk of FMD for January 1998

Figure 2: Estimated risk of reported FMD for January 1998 More interesting than the risk estimates themselves were the factors associated with predicted FMD risk and reported FMD risk in the different regions for 1998. In Africa, predicted FMD risk increased with decreasing bovine and small ruminant densities, increasing human density, decreasing pig

The Global control of FMD- Tools, ideas and ideals – Erice, Italy 14-17 October 2008

4. AUTHORS’ CONCLUSIONS

Factors influencing FMD risk and FMD reporting are different and vary by geographic region. Predicted targets for intervention: Africa – education, political voice and accountability, geographic coverage of surveillance programs (access) Americas and Australia – regional inertia Europe and Middle East – education, funding for reporting, and seasonal consistency in surveillance Asia – surveillance before animal movement and border control

meat deficit, less distance from FMD outbreaks any time during the previous year and during the same month in the previous year, poorer voice and accountability in the government, poorer literacy rate, lower GDP per capita, more borders with countries reporting FMD, fewer borders with countries not free of FMD, reports of FMD in the country in the previous year, and FMD-free status for the country in the previous year. Reported FMD risk in Africa was predicted to increase with increasing bovine and small ruminant densities, increasing human density, decreasing pig meat deficit, greater distance from cases in the same month in the previous year, less distance from FMD outbreaks any time during the previous year or in the previous month, shorter total land border length, better voice and accountability in the government, poorer literacy rate, lower GDP per capita, more borders with countries reporting FMD, reports of FMD in the country in the previous year, and FMD-free status for the country in the previous year. In the Americas and Australia, the model predicted that FMD risk increased with decreasing bovine and swine densities, increasing human density, decreasing bovine and pig meat deficits, proximity to FMD outbreaks in the same month in the previous year, poorer control of government corruption, improved literacy rate, low GDP per capita, more borders with countries reporting FMD or not free of FMD, reports of FMD in the country in the previous year, and no FMD-free status for the country in the previous year. Reported FMD risk in the Americas and Australia was predicted to increase with decreasing bovine density, increasing pig and human density, decreasing bovine meat deficit, increasing pig meat deficit, proximity to FMD outbreaks in the same month in the previous year or at any time in the previous year, shorter total land border length, poorer control of government corruption, improved literacy rate, low GDP per capita, more borders with countries reporting FMD or not free of FMD, reports of FMD in the country in the previous year, and no FMDfree status for the country in the previous year. In Europe and the Middle East, the model predicted that FMD risk increased with decreasing buffalo and small ruminant densities, decreasing human density, more land borders (as opposed to water borders) at the cell level, greater sheep and goat meat deficit, proximity to FMD outbreaks any time in the preceding year, poorer total and female literacy rate, increasing GDP per capita, and reports of FMD in the country in the previous year. Reported FMD risk in Europe and the Middle East was predicted to increase with decreasing buffalo density, increasing small ruminant and human densities, more water borders at the cell level, decreasing bovine meat deficit, increasing sheep and goat meat deficit, proximity to FMD cases in the same month in the previous year or anytime in the previous year, shorter land border length, poorer political stability, improved total literacy rate, increasing GPD per capita, and no FMD-free status for the country in the previous year. Finally in Asia, the model predicted that FMD risk increased with decreasing buffalo density, increasing pig density, more land borders at the cell level, higher deficit of sheep and goat meat, proximity to FMD cases any time in the preceding year or month, months other than the month of Eid ul-Adha, increasing land border length, improved rule of law, poorer literacy rate, reports of FMD in the country in the previous year. Reported FMD risk in Asia was predicted to increase with increasing buffalo and human densities, decreasing small ruminant density, more water borders at the cell level, higher bovine and sheep and goat meat deficits, proximity to FMD cases in the previous month, anytime in the previous year or in the same month in the previous year, the month of Eid ul-Adha, shorter land borders, and improved rule of law. Though not all of these effects were significant, several differences between the models for reported FMD and true FMD were significant and provided insight into the mechanisms behind FMD risk and reporting in the different geographic regions (Table 1).

The Global control of FMD - Tools, ideas and ideals – Erice, Italy 14-17 October 2008 5. AUTHORS’ RECOMMENDATIONS

Target interventions by region using data-based evaluations of the biological and human factors driving FMD and FMD reporting in that region. Continue using statistical models to evaluate the global FMD situation, changes in factors influencing FMD risk, and success of FMD control programs.

6. ACKNOWLEDGEMENTS

Thank you to the staff and students at the UC Davis FMD Modeling and Surveillance Laboratory for assistance in collecting the FMD presence data; to Paul Kitching and Julio Pinto for providing their expert opinions; and to Caesar Orozco, Moetapele Letshwenyo, Miryam Gallego, Bernardo Cosentino, Conrad Estrada, Willie Ungerer, and Cleopas Bamhare for providing maps of FMD-free areas. Support was provided by funding from the National Center for Medical Intelligence and the UC Davis School of Veterinary Medicine.

7. REFERENCES

[1] Christensen, R, Johnson, W, Branscum, A and Hanson, T. Bayesian Ideas and Data Analysis: An Introduction for Scientists and Statisticians. Accepted for publication. [2] CIA Factbook. Central Intelligence Agency, Washington, D.C., USA, available at https://www.cia.gov/library/publications/the-world-factbook/index.html. [3] EC-HCP, 2007. General guidance on EU import and transit rules for live animals and animal products from third countries. EC Health and Consumer Protection Directorate General, Brussels, Belgium, available at http://ec.europa.eu/food/international/trade/guide_thirdcountries2006_en.pdf. [4] FAO EMPRES and EUFMD. Focus on foot-and-mouth disease situation worldwide and major epidemiological events in 2005-2006. FAO, Rome, 2007 available at http://www.fao.org/docs/eims/upload//225050/Focus_ON_1_07_en.pdf. [5] FAOSTAT. http://www.fao.org/waicent/portal/statistics_en.asp. [6] FMD BioPortal. FMD Modeling and Surveillance Laboratory, Davis, CA, USA, 2008. Available at http://fmd.ucdavis.edu/bioportal. [7] Kass, R.E. and Raftery, A.E. 1995. Bayes Factors. JASA. 90(430): 773-795. [8] Kaufmann, D., Kraay, A., Mastruzzi, M., Governance matters VI: governance indicators for 1996-2006. World Bank Policy Research Working Paper 4280, World Bank Institute, Washington, D.C., 2007, available at SSRN: http://ssrn.com/abstract=999979. [9] OIE. Recognition of the foot-and-mouth disease status of member countries. Resolutions adopted by the international committee of the OIE during its 65th [to 66th] general session, World Animal Health Organization, Paris, 1997-1998. [10] OIE. World animal health information database (WAHID) interface.OIE, Paris, France, 2008, available at http://www.oie.int/wahid-prod/public.php?page=home. [11] ORNL. Landscan 2004. Oak Ridge National Laboratory, Tennessee, USA, 2004. [12] Wint, W. and Sumption, K. Mapping the FMD homelands: An exploratory look at global ruminant production systems associated animal movements, and FMD risk. Consultancy report, EUFMD Commission, Food and Agriculture Organization of the United Nations, Rome, Italy, 2007, presented to the General Session of the European Union Commission on Foot and Mouth Disease. Table 1: Direction of effect of and possible rationale for selected factors that appear to be associated with FMD risk and FMD reporting in different regions

Region

Africa Evidence Interpretation

"True" Risk "Reported" Risk decreases with increasing bovine density

decreases with improved voice and accountability increases if the country reported outbreaks in the previous year decreases with decreasing human density

increases with improved voice and accountability decreases if the country was not free of FMD in the previous year

- areas with high bovine density are better at controlling FMD and cases are reported more often in areas with lots of people due to access and high value of cattle - communication between the people and the government improves FMD reporting and control - countries that have FMD are likely to have FMD again and countries that have not reported FMD are not likely to report in the next year - status quo decreases with not significantly - education and economic health are related

The Global control of FMD- Tools, ideas and ideals – Erice, Italy 14-17 October 2008

The Americas and Australia

Europe and the Middle East

Asia improved literacy rate and GDP per capita

not significantly related to distance from cases in the previous month decreases with increasing bovine density: pig density insignificant greater if country was not declared free-of-FMD in the previous year increases with more borders with countries not free of FMD not significantly related to pig meat deficit not significantly related to distance from cases in the previous year decreases with increasing small ruminant density

decreases with increasing human density decreases with improved total and female-specific literacy rates not significantly related to seasonal distance from cases

increases with increasing pig density: buffalo density insignificant not significantly related to human density increases with longer land border length decreases during the month of the Eid ul-Adha not significantly related to sheep and goat meat deficit not significantly related to seasonal distance from cases related to literacy and GDP per capita

decreases farther from cases in the previous month

increases with increasing pig density: effect of bovine density insignificant effect smaller and less significant to better FMD control but do not appear to improve reporting significantly - control may be occurring without the help of outside sources - reporting of cases but not occurrence of cases is more likely in the month after and close to the location of outbreaks - possible indication of limited surveillance - areas with high bovine density have lower risk of FMD possibly due to high value of cattle while areas with high pig density are more likely to report FMD - regional inertia of infection across borders

increases with increasing pig meat deficit decreases farther from cases anytime in the previous year

increases with increasing small ruminant density

increases with increasing human density not significantly related to literacy

decreases farther from cases in the same month in the previous year increases with increasing buffalo density: pig density insignificant increases with increasing human density decreases with longer land border length

increases during the month of the Eid ulAdha increases in areas with high sheep and goat meat deficits decreases farther from cases in the same month in the previous year - areas that import pig meat are more likely to report FMD possibly due to recognition at slaughter or better funding - reporting (not occurrence) of cases is somewhat consistent over the year, but may be localized

- increased reporting and decreased risk are associated with small ruminant density - a possible indication of successful control programs in small ruminants - indicates better control and reporting programs where more people are present

- education, and specifically in women, is associated with reduced FMD risk but not reporting - a possible indication of control without outside assistance - proximity to locations that had FMD outbreaks a year ago increases the chances of reporting an outbreak - surveillance may be seasonal - areas with higher pig density have a higher FMD risk possibly due to greater transmission in pigs, while reporting appears to be better in buffalo - reporting of FMD is more likely in areas with high density of humans - possibly due to better surveillance - countries with longer land borders have poorer FMD control and poorer FMD surveillance - likely due to a lack of resources for border control and surveillance - areas that import sheep and goats, specifically in the month of the Eid, are likely to report cases of FMD, but cases are less likely to occur overall in the month of the Eid possibly because the animals have been shipped and slaughtered in that month - proximity to locations that had FMD outbreaks a year ago increases the chances of reporting an outbreak - surveillance may be seasonal, possibly related to the Eid