1

Risk assessment of vector-borne diseases for Public Health governance 1

2

L. Seddaa,d*, D. Morleya,e, M. A. H. Braksb, L. De Simonec, D. Benza, and D. J. 3

Rogersa 4

5

a Department of Zoology, University of Oxford (UK), South Parks Road, OX1 3PS, Oxford, 6

United Kingdom. 7

b Centre for Zoonoses and Environmental Microbiology, National Institute for Public 8

Health and the Environment (RIVM) P.O. Box 1, 3720 BA, Bilthoven, the Netherlands. 9

c Surveillance and Response Support Unit (SRS), European Centre for Disease Prevention 10

and Control (ECDC), Tomtebodavägen 11 A, 171 83 Stockholm, Sweden. 11

d present address: Geography and Environment, University of Southampton, University 12

Road, SO17 1BJ, Southampton, United Kingdom. 13

e present address: MRC-PHE Centre for Environment and Health, Imperial College London, 14

St. Mary’s Campus, W2 1PG, London, United Kingdom. 15

16

* Corresponding author. L.Sedda@soton.ac.uk, Tel. +44(0)2380599622, Fax 17

+44(0)2380593295. 18

19

2

Abstract 20

Objectives. In the context of public health, risk governance (or risk analysis) is a 21

framework for the assessment and subsequent management and/or control of the danger 22

posed by an identified disease threat. Generic frameworks in which to carry out risk 23

assessment have been developed by various agencies. These include monitoring, data 24

collection, statistical analysis and dissemination. Due to the inherent complexity of disease 25

systems, however, the generic approach must be modified for individual, disease-specific 26

risk assessment frameworks. 27

Study design. The analysis was based on the review of the current risk assessments of 28

vector-borne diseases adopted by the main Public Health organisations (OIE, WHO, 29

ECDC, FAO, CDC etc…). 30

Methods. Literature, legislation and statistical assessment of the risk analysis 31

frameworks. 32

Results. This review outlines the need for the development of a general public health 33

risk assessment method for vector-borne diseases, in order to guarantee that sufficient 34

information is gathered to apply robust models of risk assessment. Stochastic (especially 35

spatial) methods, often in Bayesian frameworks are now gaining prominence in standard 36

risk assessment procedures because of their ability to assess accurately model uncertainties. 37

Conclusions. Risk assessment needs to be addressed quantitatively wherever possible, 38

and submitted with its quality assessment in order to enable successful public health 39

measures to be adopted. In terms of current practice, often a series of different models and 40

analyses are applied to the same problem, with results and outcomes that are difficult to 41

3

compare because of the unknown model and data uncertainties. Therefore, the risk 42

assessment areas in need of further research are identified in this article. 43

44

Keywords: risk governance, risk assessment, public health governance, vector-borne 45

diseases, statistical analysis. 46

47

4

1. Introduction 48

Public Health (PH) can be defined as the activities engaged in by governments and 49

organisations to prevent disease and to ensure a healthy population 1. This includes the 50

assessment and monitoring of the health of communities and populations at risk, 51

intervention, education, and formulation of public policies, and encompasses all issues that 52

may be detrimental to health 2. In terms of Public Health, risk has been defined by medical 53

epidemiologists and health organizations as “the probability of disease developing in an 54

individual in a specified time interval” 3. More generally, the Society for Risk Analysis 55

(SRA) 4 defined risk as “the potential for realization of unwanted, adverse consequences to 56

human life, health, property, or the environment; estimation of risk is usually based on the 57

expected value of the probability of the event occurring times the consequence of the event 58

given that it has occurred”. In econometrics, risk is quantified by the “absolute value of 59

expected losses” 5. Notice that in these latter two definitions the probabilistic concept of 60

risk is conflated with some idea of the (fixed or calculable) economic cost outcome should 61

that risk be realised. 62

PH risk governance is a framework 6 that includes risk assessment (or risk 63

estimation), risk management and risk communication. In this manuscript we focus on one 64

category of public health challenges, the risk assessment of vector-borne diseases (human 65

and/or animal illness transmitted by arthropods) which, at least in the tropics, have a huge 66

impact on human health and well-being (for example, WHO estimated there were 627,000 67

deaths among 207 million cases of malaria in 2012 7). Emerging vector-borne diseases are 68

of increasing concern in Europe 8 and North America (West Nile virus, chikunguya, 69

malaria and dengue for instance) some with autochthonous cases 9. 70

5

Especially in the case of vector-borne diseases, because of their complexity, the metric used 71

for risk is not uniquely defined. Often risk is synonymous with vector or host suitability 10, 72

the basic reproduction number R0 11, vector capacity 12, vector presence/absence 13, 73

prevalence or incidence of the disease 14 or many others. The same diversity is found in 74

model selection, data resolution, the predictor variables, their transformation, and the input 75

parameters (e.g. models coefficients applied to different areas/events) used to model risk, 76

even for the same disease (e.g. 15 for Lyme disease). Hence a deep understanding of the 77

various metrics and models used for estimating risk is of primary importance, especially 78

when previously obtained parameters, values and models are applied outside the domain of 79

the original model fitting 15 (e.g. in the case of newly emerging diseases). 80

The goal here is to integrate risk assessment or estimation theory (and models) for 81

vector-borne diseases with the present regulatory, and sometimes mandatory, system of risk 82

governance, as defined by the main health organizations. 83

84

2. Risk governance in international public health organizations and some national 85

agencies. 86

Table 1 presents some key contributions to general risk-related policy over the last 87

fifty years. From the initial basic statements of risk governance guidelines, procedures have 88

developed into clear conceptual frameworks allowing a standard approach to be adopted. 89

Most recently the complementary roles of quantitative and qualitative methods have been 90

directly addressed along with the understanding that an estimation of uncertainty is vital to 91

any risk analysis. 92

6

The concept of risk governance as a combination of risk assessment, risk 93

management and risk communication (or “translating knowledge” 16 into advice for 94

informative/legal actions 17) originated in the 1960s and 1970s when concerns were raised 95

over environmental carcinogens and food additives 18 19 20 21 22. Subsequently risk 96

governance concepts were formulated by individual scientists19, and by national and 97

international agencies20, with a concentration on cancer and harmful chemicals21, 22. In the 98

1990s, risk assessment became mandatory in Europe for environmental, epidemiological 99

and food health threats (i.e. Council Directive 89/391/ECC, the Decision No 2119 of 100

1998/EC, the European Commission Regulation 178/EC of 2002), was enshrined within the 101

European Union Risk Assessment System and was managed by DG SANCO. 102

The fundamentals of risk governance are similar in the various frameworks of Table 1 103

and based on the concepts defined by the Red Book and by the CODEX of the 104

FAO/WHO23 23. With these documents the division of risk governance into risk 105

assessment, risk management and risk communication becomes the standard form for risk 106

governance for any type of risk. 107

None of the frameworks presented in Table 1 imposes a specific method of risk 108

assessment. Indeed, a unique risk assessment method for Public Health risk governance 109

does not yet exist. The following section outlines the current statistical approaches to risk 110

assessment as a contribution to developing such a method. It concentrates on vector-borne 111

diseases because although these have historically been neglected in many risk assessment 112

frameworks they are likely to become increasingly important in temperate regions in the 113

future. 114

115

7

3. Risk assessment for Public Health risk governance. 116

To date, risk assessment of vector-borne and other diseases for Public Health applications 117

has depended heavily upon the modellers and what they can and cannot do with the data 118

available. The choice of the model depends on the aetiology of the disease in question. For 119

example, when aetiology is unknown, studies are usually population-based (e.g. 120

smoking/pollutants related cancers). Usually very large sample sizes are required to 121

remove obvious biases and to reveal what are often very low incremental probabilities of 122

disease risk. In most vector-borne diseases the aetiology is well established. Insect or tick 123

‘vectors’ pick up infections (i.e. become infected) when they take blood from infectious 124

vertebrate hosts. The vectors in turn become infectious (i.e. capable of transmitting the 125

infection to another vertebrate host) only after a period of incubation which may take 126

several days (insects) or even months (ticks) and is referred to as extrinsic incubation 127

period (EIP). Hence a minimum of three species are involved in vector-borne disease 128

transmission (host, vector and pathogen). In fact such transmission often involves 129

numerous and complex vector and zoonotic (domestic and wild animal) components. In 130

these cases even if risk assessment can be more individual-based, or based on identifiable 131

sub-groups, for example focussed only on those people living where vectors can be found, 132

the number of parameters and variables needed would be greater than is typically used in 133

other sorts of public health models 12, 24. More complex systems need more research 134

investment to reach the same level of understanding but vector-borne diseases have been 135

neglected in comparison with other diseases (e.g. cancer), probably because of their lack of 136

importance in the developed world. 137

138

8

In 2006, and following an outbreak of the mosquito-borne disease chikungunya 139

(CHIK)25 in Asia, destination of many tourists and others from Europe, the European 140

Centre for Disease Prevention and Control (ECDC) conducted a CHIK risk assessment for 141

Europe. The risk assessment was based on the knowledge that infected and infectious 142

travellers were returning to Europe, in some cases to places where a known vector of 143

chikungunya, the Asian tiger mosquito Aedes albopictus, had already established (in 144

Europe, this mosquito was first reported in Albania in 1979, and in Italy in 1990). By 145

studying the current numbers of recorded cases in endemic areas as well as imported cases 146

and the presence of local vector species, the ECDC concluded there was a risk of local 147

acquisitions of CHIK virus in Europe, although the magnitude of this risk could not be 148

determined 26. Quantitative models were not applied because of the lack of knowledge of 149

the response of the virus to the environmental conditions of a new geographic area, the 150

preventative measures on the parts of both travellers and health professions, the potential of 151

further spread in southern Europe of Ae. albopictus and finally the competence of local 152

mosquitoes to transmit the virus. In 2009, following a large outbreak of CHIK in Italy in 153

2007 and after a subsequent large-scale entomological survey, the ECDC published a first 154

risk map for Ae. albopictus in Europe 27, based on a random forest model and multi-criteria 155

analysis applied to entomological and environmental datasets. As in all other cases, 156

however, a risk map of a putative vector is not the same as a risk map of the disease it 157

transmits. ‘Anophelism without malaria’ (i.e. the occurrence of the vector Anopheles 158

without the malaria) is a familiar concept to malariologists, and a similar effect is likely to 159

apply to all other vector-borne diseases. 160

9

Disease pathogens potentially transmitted by many different mosquito species are 161

even less likely to be described by risk maps of just one, or even several of their vector 162

species. For example, the European Union has made several attempts to assess the risk to 163

Europe of Rift Valley fever (the transmission of which in Africa involves sevarla different 164

mosquito species) but, due to the complexity of the disease epidemiology and the 165

difficulties in the transmission parameterization, the risk assessments were predominantly 166

qualitative 28 29. 167

168

Similar problems arise with diseases of veterinary importance, especially those of 169

high economic impact such as bluetongue (BT), which provoked an extensive legislative 170

response (Directive 75/EC of 2000, EC Regulation 178 of 2002, Commission Decision 171

393/EC of 2005, Regulation 1266/EC of 2007; Decision, 367/EC, Regulation 394/EC of 172

2008, Regulation 1304/EC of 2008, Decision 655/EC of 2008, Decision 19/EC of 2009, 173

Regulation 1156/EC of 2009); or foot-and-mouth in the UK in 2001. In both cases control 174

activities against the diseases were eventually informed by predictive risk models each of 175

which subsequently came in for a certain amount of criticism because of the ways in which 176

the models were formulated (including the assumptions on which the models were based), 177

and the values of the models parameters used. 178

179

These examples show the difficulties in applying model-based solutions to human 180

and non-human, vector-borne and directly transmitted disease outbreak emergencies when 181

only inadequate or incomplete data are available. This will inevitably be the case with 182

emerging diseases (for example chikungunya, Rift Valley fever and West Nile virus in 183

10

Europe) but may also apply even to neglected but long-established diseases, such as Lyme 184

disease 8, 15. 185

The examples clearly show that, in the face of an imminent public or veterinary 186

health threat, control agencies have little time to worry about the characteristics of disease 187

models. Instead, they must be seen to be doing something to combat the diseases, whether 188

or not this is informed by quantitative models. Sometimes this involves excessive 189

application of the precautionary principle, for example the localised and repeated use of 190

insecticide which although it diminishes disease risk may not eliminate it entirely but 191

certainly does create other environmental and health risks 30. The greater the impact of an 192

event, or the longer its duration, the more likely an inappropriate control (either too much 193

or too little; the precautionary approach encourages the former) will be applied. Policy 194

must therefore encourage the development of robust models in advance of any new threat 195

and, ideally, these models should be tested during simulation exercises 15, as was done in 196

2004 in the aftermath of the 2001 foot-and-mouth outbreak in the UK and following no 197

fewer than three independent reports into the outbreak31 and one Department of Health 198

longitudinal study on the health and social impacts of it32. 199

But what should these robust models look like, and how may they be developed? The 200

final section explores some of the options. 201

202

4. Models for Public Health risk governance. 203

The distinction made in the previous section, between models for diseases without or 204

with known aetiologies, highlights the dichotomy between statistical and biological 205

approaches to disease modelling. Statistical models look for statistical associations 206

11

between a dependent variable (e.g. the number or percentage of disease cases) and a suite 207

of independent variables that may be either correlated with risk, indicators of risk or causal 208

drivers of risk (the distinction between these three different types of independent variables 209

is not obvious at the start of many studies but tends to become clearer over time, as was the 210

case with lung cancer). The statistical model of risk therefore seeks robust statistical 211

descriptions of levels of risk based on the measured levels of the independent variables. 212

Various kinds of regression and odds ratio models can be employed here. 213

In contrast to statistical models, biological models consist of components, each of which 214

captures one or other important biological feature of the know infection pathway. For 215

example, a model for the extrinsic incubation period (defined above) will incorporate 216

temperature as a causal variable determining the duration of the EIP. During the EIP, 217

however, the insect may die either from abiotic (temperature, rainfall) or biotic (predation) 218

effects each of which may be incorporated into full biological models of disease 219

transmission. Characteristic of many biological systems are thresholds (pathogens will not 220

develop at all below a threshold temperature), break-points (mortalities may be low at low 221

densities and then increase above a certain level of abundance of competitors, parasites or 222

predators of the vectors) and non-linearities (insect mortality rates increase at increasing 223

rates at the upper end of their thermal tolerance ranges). The ideal biological model must 224

therefore be able to describe all aspects of each and every threshold, break-point and non-225

linearity at all points of the transmission pathway and this is often beyond the limits of our 226

present knowledge and capabilities. Instead, relatively simplistic but still biologically - 227

based models can go a long way towards describing the overall characteristics of diseases, 228

if not their finer points. Classic examples of simple but powerful biological models for 229

12

vector-borne diseases involve calculation of the key epidemiological variable, R0, the 230

disease’s basic reproductive number, or the number of secondary cases arising in an 231

entirely susceptible population from one case of the disease at the present time. This index 232

can be used to predict if a vector-borne disease can establish after introduction in a certain 233

area and under certain climatic and environmental conditions 33. It has several advantages 234

over statistical methods, such as the clear biological and epidemiological interpretation of 235

its values, and the flexibility for simulating different scenarios. However, even the 236

relatively simplified R0 equation requires estimates of many different parameters and the 237

knowledge of the relevant biological processes involved (for example, the R0 equation for 238

insect vectors is quite different from that for ticks). In addition, theoretical studies show 239

that R0 is related to disease prevalence at equilibrium (i.e. the former might therefore be 240

used to predict the latter). But it is a moot point as to how often diseases are in fact in 241

equilibrium, and therefore how often the R0 approach is applicable; vector-borne diseases 242

are often seasonal and many emerging and re-emerging vector-borne diseases of interest are 243

clearly not in equilibrium. 244

An alternative approach for vector-borne diseases is therefore to fall back on the sorts 245

of statistical methods applied to diseases of unknown aetiology. This would appear to be a 246

backward step (why ignore all the information we have on causal pathways in the 247

transmission of vector-borne diseases?) but it may be the only approach we can take when 248

we are unclear about key aspects of transmission, all of which must be understood for the 249

biological approach to succeed. For example, West Nile virus in the USA infects at least 250

100 species of mammals and birds and is potentially transmitted by up to 70 or more 251

different mosquito species; what chance, ever, is there of being able to quantify the myriad 252

13

interactions there might be between this number of vectors and hosts? In such a context, 253

statistical models ignore many of the details of biological transmission and look for direct 254

correlations between environmental variables and vector or disease outcomes 10. This 255

approach clearly ‘short-circuits’ the often tortuous biological pathways involved in 256

transmission, and depends for its success on close, predictable and unchanging links 257

between predictor and predicted variables. That this approach is often successful is due 258

entirely to the sensitivity of many vector-borne diseases to climate (most of the parameters 259

of the R0 equation for vector-borne diseases are vector-related and hence sensitive to 260

climate and environment). Risk assessments here are based on modelled correlations 261

between the vector’s distribution and or abundance, or past disease presence/incidence, and 262

sets of environmental variables. In the first case (based on vector distribution/abundance), 263

analyses are excellent predictors of risk to individuals. It is, however, difficult to translate 264

them into measures of human disease incidence or prevalence, both population-level 265

measures of risk. In the second (based on past incidence/prevalence) the risk assessment is 266

clearly based on the past exposure of the local human population to disease risk, something 267

that is affected not just by the absolute risk in each area, but by the amount of human 268

exposure to that risk, dependent on both human population density and behaviour. The 269

resulting risk assessment, therefore involves the often unacknowledged component of 270

human behaviour, which is sometimes more important than human population in 271

determining disease incidence or prevalence 34. 272

Commercial trade and human travel add additional levels of complexity. Examples 273

here include the connectivity/mobility analysis of humans (e.g. air travel, 35, and goods 24) 274

in which risk is often calculated from a crude or probabilistic combination of suitable 275

14

habitat in the arrival area for the pathogen or vector (brought in by tourism or trade) and the 276

rate at which such importation occurs. 277

As a result of the application of the above approaches, risk can be expressed as 278

incidence, prevalence, occurrence, cumulative cases (as for the West Nile Virus map used 279

by CDC) or, alternatively, as other metrics that generally represent a portion of the disease 280

transmission process, for example the probability of pathogen establishment according to 281

the probability of introduction24, 35, the probability of contact between host and vectors but 282

not humans 36 or between host capacities (a rate of vector infection from the host species), 283

vector-host feeding preferences and, finally, human biting rates per vector 37. Risk metrics 284

are hence based on the information available (see the attempt of 38 to produce a uniform 285

scheme) and are rarely expressed as a probability (apart from the few examples given here), 286

which is the canonical form for risk as defined for public health (see the introduction 287

section). 288

289

The statistical and mechanistic approaches to risk modelling and prediction are many 290

and varied, including discriminant analysis, generalized linear models (e.g. logistic 291

regression, autoregressive regression, Poisson models etc…), generalized additive models, 292

mixed models, tree based classifications, fractals, spectral density, wavelets, agent based, 293

cellular automata, maximum entropy, multicriteria analysis, community models among 294

others 12, 24, 36, 39, 40. Examples of applications of these models can be found in the 295

quantitative risk assessment as defined by the World Organisation for Animal Health (OIE) 296

(Terrestrial Animal Health Code 41); WHO (numerical approaches to the quantification of 297

risk 42); International Risk Governance Council (IRGC) 43; EFSA 44; International 298

15

Organisation for Standardization (ISO 31000)45; and the National Research Council of the 299

United States 46. It is well established that different models often give different outputs 300

(e.g. the dengue models compared in 47), hence a procedure for their validation and 301

comparison is needed. 302

A further complication, which is often ignored in vector and disease risk mapping, is 303

that of spatial dependency of the disease, which arises for two main reasons. Firstly, simple 304

geographic proximity means that epidemiological outcomes often depend on disease events 305

occurring in nearby locations (e.g. pathogen transmission may occur between areas that are 306

close to each other, or infected vectors might fly, or infected humans move, between them). 307

Secondly the environmental conditions that favour the occurrence of a vector or disease in 308

one area are likely to be similar in nearby areas and so promote the occurrence of the same 309

disease in those areas, too. Whatever the cause of spatial dependency is, it may be used to 310

improve the predictions of vector-borne disease models to render them truly spatial. Spatial 311

dependency is included in models in many different ways, from the simple auto-logistic 312

regression approach of considering the occurrence of the species only in the most adjacent 313

grid squares to the more complex approach of using the techniques of kriging and cokriging 314

(directly or combined with deterministic models). The unwary biologist or modeller should 315

be warned that kriging and cokriging often applies only to predicting errors from a 316

modelled ‘mean’ surface, the latter imagined to be constant or trending in a simple way 317

(e.g. dependent on environmental variables) 48. The biological interest is therefore really in 318

the trend surface rather than in the kriging results 49. Kriging thus contributes an important 319

and usually neglected component to risk maps; the prediction of errors indicating the 320

variability of the modelled result at different points in space (and time, with spatio-temporal 321

16

models), and hence the statistical significance of the trend. The size of such errors 322

generally depends upon the amount and variability of the data available; errors are 323

minimized when there is a sufficient amount of local data, all with similar mean values 324

(which is not always the case for vector-borne diseases such as malaria) 50. 325

Today we are still experiencing risk maps 51 52 based simply on the un-modelled 326

distribution of diseases, pathogens or conditions, or real-time data from online warning 327

systems and media sources (Table 2), or from informal exchanges between public health 328

professionals ("Event-based" surveillance) 53. Despite increasing use of disease mapping, 329

for which often quality cannot be evaluated, it is urgent to define a policy for disease risk 330

mapping assessment (within the risk governance framework) in order to obtain products of 331

similar statistical quality. 332

Uncertainty is a common feature of risk assessment of meteorological events and 333

earthquakes, but it is usually not applied in vector-borne disease risk mapping. In the past, 334

uncertainty was obtained from sensitivity analyses by simply varying (within fixed ranges) 335

the values chosen for the parameters and variables. Rarely do sensitivity analyses consider 336

variation of more than one parameter/variable at a time. Interactions between variables may 337

be investigated using hyper-cube sampling of variables, but gives no real indication of the 338

likely variability of each of the parameters in the field 54. 339

Today, the most successful models use a Bayesian approach to predicting both the level of 340

risk and the uncertainty of that prediction 55. This approach is usually applied when 341

knowledge of the process is available and converted into distributional forms, but is also 342

used for hypothesis testing and uncertainty measurement. These new Bayesian approaches 343

are ideal for the relatively small datasets often found in epidemiology, for which the 344

17

frequentist approaches are unable to include prior knowledge 56 although they are robust for 345

small sample size. Frequentist studies are based on the hypothetical question “Given ten 346

thousand versions of reality, on how many occasions would we observe an outcome as 347

extreme as that shown by the data?” If the occasions are very few, then the result is likely 348

to be ‘significant’, i.e. it is unlikely to be a member of the ten thousand versions of reality 349

generated by the hypothesis being tested. Unfortunately, however, there is only one version 350

of reality and so the above question remains a hypothetical one. The one version of reality, 351

that we do have, generates the data that we record. These data may have been produced by 352

a variety of different mechanisms which we can test in the Bayesian modelling approach to 353

answer the question; “Given this particular dataset, is hypothesis/model A more likely than 354

hypothesis/model B?” Not only do Bayesian statistics allow us to distinguish between 355

alternative hypotheses, they also involve estimation of the errors of the different parameters 356

involved, allowing the output models not only to predict mean risk, but also the errors and 357

uncertainties of those mean risks 34, 57. From the practical point of view, the main 358

differences between the Bayesian and the more traditional frequentist approach are the use 359

of prior and posterior distributions. The latter is the main output of the Bayesian approach 360

and expresses as a probability, the weighted average of the prior (a priori knowledge) and 361

the data (through its likelihood) via the Bayesian theorem 58. 362

363

A third model category exists in Public Health applications, based on expert opinions, 364

or other experience, and referred to as participatory models 59. An example is the 365

vaccination-advice map for yellow fever 60 based on expert opinion that decided specific 366

thresholds for certain environmental variables thought to be key to transmission (for 367

18

example, elevation, land cover etc…) 61. This map categorises risk into four classes: 368

endemic, transitional, low risk and no risk 61. Vaccination is “recommended” for those 369

areas that are classified as endemic and transitional, and also for low risk areas if a 370

prolonged stay is expected. In brief, this semi-quantitative risk assessment is the result of a 371

complex process involving data acquisition, data analysis and data discussion in which 372

modellers, national experts and epidemiologists are involved 60. The role of the health 373

practitioner using such a map is to merge the information given by the guidelines with 374

individual characteristics of the person to be exposed to the disease risk (age, gender, 375

medical history, vaccination history) and trip information (destination, departure date, 376

duration of the travel, purpose of travel) 62. Whilst the final decision here (to vaccinate or 377

not) is based on a subjective weighing of the evidence by the medical practitioner or health 378

agency involved, this decision is likely to be better informed if based upon more accurate, 379

quantitative risk maps. The unplanned surveillance and control in Sudan, for areas that are 380

recognised of endemic transmission for yellow fever, may have contribute to the recent 381

uncontrolled outbreaks in Sudan, which required a mass-vaccination treatment of the 382

population 63. 383

384

In summary, while risk governance is a framework that is very homogeneous between 385

international organisations and scientists 1, the risk assessment component is not 386

standardised, and often not defined at all. For example, in the OIE terrestrial code a list of 387

statistics necessary for risk assessment is given without mention of their uncertainty. EFSA 388

describes the full risk assessment framework, which includes uncertainty, but is not flexible 389

enough to be adapted for general use. Other agencies require semi-quantitative approaches 390

19

with a strong stakeholder component (IRGC and WHO). The seminal work of 1 adapted 391

the IRGC risk governance framework to the case of vector-borne diseases for which they 392

provide generic, but not methodological, guidelines for a pre-assessment and appraisal of 393

the risk. Apart from the useful indication on the data requested for public health utility, the 394

authors did not however, explore the issues related to the existing variability in the data, 395

methods and outputs used for risk estimation. Risk assessment for vector-borne diseases 396

wss only defined in its general aspects, and a framework which guarantees high quality 397

disease mapping and comparable analyses, was missing. 398

399

5. Conclusions. 400

Risk governance is the main component of PH measures (prevention, management, 401

surveillance and communication) and is scientifically and legally defined. Although the 402

same basic framework is followed, each risk assessment tends to be uniquely determined by 403

the details of the particular disease under study, the data available for it and the region and 404

time period over which those data were collected. In a recent review on the impact of 405

drought on vector-borne diseases for UK 64, the authors underlined the undefined risk 406

associated with climate change and human adaptation due to the lack of relevant data 65. In 407

addition, the competence of existing or newly introduced insect vectors in Europe to 408

transmit diseases that are currently confined mostly to the tropics 36 11, 66 are unknown as 409

they ways in which they are transported 24, 35, 37. At present time, when different models 410

and analyses are applied to the same problem, they often produce results that are difficult to 411

compare quantitatively because of unknown model and data uncertainties. 412

20

Hence, new dynamic and flexible approaches are required, determined by the Public 413

Health framework and the inherent complexity and unique characteristics of each system 414

being investigated. 415

Anthropological, zoological or entomological (or a combination of these) disease risk 416

assessment 67 needs to be addressed quantitatively, wherever possible, and submitted with a 417

quality assessment in order to evaluate tolerability and adaptability of the risk1; and to 418

enable successful public health measures to be adopted 68, since simple interventions are 419

often impossible (for many vector-borne diseases there is no available treatment or vaccine 420

69). 421

Successful application of control measures in the future depends on the reliability of 422

the uncertainty estimations 40. Even if uncertainty sometimes cannot be reduced 70, it is 423

important for policy-makers to have an accurate estimate of it to support action if the risk 424

estimates warrant it, in order to avoid over- and under-estimations of risk 49. 425

In conclusion we make the following recommendations for the future: 426

1) A standard risk assessment within a risk governance framework for vector-borne 427

diseases as proposed by 1 should be produced and accepted, that includes 428

experiences and lessons from the various agencies involved in risk assessment 429

(e.g. OIE, FAO, EFSA, WHO, SANCO) and from OneHealth actions 71; 430

2) A comparative review should be carried out of Public Health model selection 431

decisions and model evaluations (as done by 72 for species’ distributions); 432

3) Quantitative approaches should be used in participatory analyses leading to risk 433

assessment 73, 74; 434

21

4) In conformity with the definition of risk in public health, risk should be expressed 435

as a probability; 436

5) All models used for making Public Health decisions should include estimates of 437

model uncertainty 75; 438

6) The spatial and temporal validity of models (i.e. up to what distances and times 439

are the model error levels acceptable) should always be assessed; 440

7) Finally, data should routinely be gathered that can contribute to Public Health 441

preparedness for future outbreaks of vector-borne and other diseases. 442

preparedness for which quality and availability of the data need to be assessed for 443

future emergencies. 444

445

Acknowledgments 446

L.S. and D.J.R. are supported by the European Union grant HEALTH-F3-2010-261504 447

EDENext; this paper is catalogued by the EDEN Steering Committee as EDENext XXXX 448

(http://www.edenext.eu). The contents are the sole responsibility of the authors and do not 449

necessarily reflect the views of the European Commission. 450

451

Competing interests: None declared. The funders had no role in the study design; in the 452

collection, analysis and interpretation of data; in the writing of the manuscript; and in the 453

decision to submit the manuscript for publication. 454

455

Ethical approval: Not required. This research is a review and does not include human 456

subjects. 457

22

458

23

References 459

1. Schmidt K, Dressel KM, Niedrig M, Mertens M, Schule SA, Groschup MH. Public 460

health and vector-borne diseases - a new concept for risk governance. Zoonoses and public 461

health. 2013; 60:528-38. 462

2. Fleming ML, Parker E. Introduction to Public Health: Elsevier Health Sciences 463

APAC; 2009. 464

3. Dicker R, Coronado F, Koo D, Gibson Parrish R. Principles of epidemiology in 465

public health practice. 2006:512. 466

4. Thompson KM, Deisler PF, Jr., Schwing RC. Interdisciplinary vision: the first 25 467

years of the Society for Risk Analysis (SRA), 1980-2005. Risk Anal. 2005; 25:1333-86. 468

5. Stärk KDC, Regula G, Hernandez J, Knopf L, Fuchs K, Morris RS, et al. Concepts 469

for risk-based surveillance in the field of veterinary medicine and veterinary public health: 470

review of current approaches. BMC Health Serv Res. 2006; 6:20. 471

6. Gostin LO, Fund MM. Public Health Law and Ethics: A Reader: University of 472

California Press; 2010. 473

7. WHO. World Malaria Report 2013: World Health Organization; 2014. 474

8. Medlock J, Jameson L. Ecological approaches to informing public health policy and 475

risk assessments on emerging vector-borne zoonoses. Emerging health threats journal. 476

2010; 3:e1. 477

9. Nasci RS. Movement of Chikungynya Virus into the Western Hemisphere. 478

Emerging Infectious Diseases. 2014; 20:1394-5. 479

10. Brownstein JS, Rosen H, Purdy D, Miller JR, Merlino M, Mostashari F, et al. 480

Spatial analysis of West Nile virus: rapid risk assessment of an introduced vector-borne 481

zoonosis. Vector Borne Zoonotic Dis. 2002; 2:157-64. 482

11. Robin CA, Lo Iacono G, Gubbins S, Wood JL, Newton JR. The accuracy of the 483

National Equine Database in relation to vector-borne disease risk modelling of horses in 484

Great Britain. Equine Vet J. 2013; 45:302-8. 485

12. Zavaleta JO, Rossignol PA. Community-level analysis of risk of vector-borne 486

disease. Transactions of the Royal Society of Tropical Medicine and Hygiene. 2004; 487

98:610-8. 488

13. Romi R, Boccolini D, Menegon M, Rezza G. Probable autochthonous introduced 489

malaria cases in Italy in 2009-2011 and the risk of local vector-borne transmission. Euro 490

Surveill. 2012; 17. 491

14. Winters AM, Eisen RJ, Delorey MJ, Fischer M, Nasci RS, Zielinski-Gutierrez E, et 492

al. Spatial risk assessments based on vector-borne disease epidemiologic data: importance 493

of scale for West Nile virus disease in Colorado. The American journal of tropical medicine 494

and hygiene. 2010; 82:945-53. 495

15. Lorenz A, Dhingra R, Chang HH, Bisanzio D, Liu Y, Remais JV. Inter-model 496

comparison of the landscape determinants of vector-borne disease: implications for 497

epidemiological and entomological risk modeling. PloS one. 2014; 9:e103163. 498

16. Matthews GW, Markiewicz M, Beitsch LM. Legal frameworks supporting public 499

health department accreditation: lessons learned from 10 states. J Public Health Manag 500

Pract. 2012; 18:E8-E16. 501

17. Bennett P. Risk Communication and Public Health: OUP Oxford; 2010. 502

24

18. Committee on Risk Assessment of Hazardous Air Pollutants, Board on 503

Environmental Studies and Toxicology, Commission on Life Sciences. Science and 504

Judgment in Risk Assessment: National Academies Press; 1994. 505

19. Rowe WD. An anatomy of risk. New York ; London: Wiley; 1977. 506

20. EPA. Health Risk and Economic Impact Assessments of Suspected Carcinogens: 507

Interim Procedures & Guidelines. In: Agency USEP, editor.: Federal Register; 1976. p. 508

21402-5. 509

21. OTA. Assessment of technologies for determining cancer risks from the 510

environment. Washington, D.C.: U.S. Office of Technology Assessment1981 June. 511

22. National Research Council N. Risk Assessment in the Federal Government: 512

Managing the Process: The National Academies Press; 1983. 513

23. Joint Consultation FAO and WHO Expert FW. Application of Risk Analysis to 514

Food Standards Issues. Geneva: WHO WHO: FNU/FOS/95.31995. 515

24. Thomas SM, Tjaden NB, van den Bos S, Beierkuhnlein C. Implementing cargo 516

movement into climate based risk assessment of vector-borne diseases. International 517

journal of environmental research and public health. 2014; 11:3360-74. 518

25. Burt FJ, Rolph MS, Rulli NE, Mahalingam S, Heise MT. Chikungunya: a re-519

emerging virus. Lancet. 2012; 379:662-71. 520

26. Depoortere E, Coulombier D. Chikungunya risk assessment for Europe: 521

recommendations for action. Euro Surveill. 2006; 11:E060511 2. 522

27. ECDC. Development of Aedes albopictus risk maps. 2009. 523

28. EFSA Panel on Animal Health and Welfare. Scientific Opinion on Rift Valley 524

Fever. EFSA Journal. 2013; 11:1-48. 525

29. EFSA Panel on Animal Health and Welfare. The risk of Rift Valley Fever incursion 526

and its persistence within the community. EFSA Journal. 2005:1-128. 527

30. Matthews G, Zaim M, Yadav RS, Soares A, Hii J, Ameneshewa B, et al. Status of 528

legislation and regulatory control of public health pesticides in countries endemic with or at 529

risk of major vector-borne diseases. Environ Health Perspect. 2011; 119:1517-22. 530

31. Donaldson A, Lee R, Ward N, Willkinson K. Foot and mouth - five years on: the 531

legacy of the 2001 foot and mouth disease crisis for farming and the British countryside. 532

Newcastle Upon Tyne: University of Newcastle Upon Tyne2006 2006. 533

32. Peck DF. Foot and mouth outbreak: lessons for mental health services. Advances in 534

Psychiatric Treatment. 2005; 11:270-6. 535

33. Hartemink N, Vanwambeke SO, Heesterbeek H, Rogers D, Morley D, Pesson B, et 536

al. Integrated mapping of establishment risk for emerging vector-borne infections: a case 537

study of canine leishmaniasis in southwest France. PloS one. 2011; 6:e20817. 538

34. Lawson A, Biggeri A, Böhning D, Lesaffre E, Viel J-F, Bertollini R. Disease 539

mapping and risk assessment for public health: John Wiley & Sons; 1999. 540

35. Huang Z, Das A, Qiu Y, Tatem AJ. Web-based GIS: the vector-borne disease 541

airline importation risk (VBD-AIR) tool. International journal of health geographics. 2012; 542

11:33. 543

36. Tran A, Ippoliti C, Balenghien T, Conte A, Gely M, Calistri P, et al. A geographical 544

information system-based multicriteria evaluation to map areas at risk for Rift Valley fever 545

vector-borne transmission in Italy. Transbound Emerg Dis. 2013; 60 Suppl 2:14-23. 546

25

37. Bosch J, Munoz MJ, Martinez M, de la Torre A, Estrada-Pena A. Vector-borne 547

pathogen spread through ticks on migratory birds: a probabilistic spatial risk model for 548

South-Western europe. Transbound Emerg Dis. 2013; 60:403-15. 549

38. Hay SI, Battle KE, Pigott DM, Smith DL, Moyes CL, Bhatt S, et al. Global 550

mapping of infectious disease. Philos Trans R Soc Lond B Biol Sci. 2013; 368:20120250. 551

39. Meltzer MI. The potential use of fractals in epidemiology. Preventive veterinary 552

medicine. 1991; 11:255-60. 553

40. Lawson AB. Statistical Methods in Spatial Epidemiology: Wiley; 2006. 554

41. World Organization for Animal Health O. Terrestrial Animal Health Code 2012: 555

Renouf Publishing Company Limited; 2012. 556

42. Ezzati M, Lopez AD, Rodgers A, Murray CJL, editors. Comparative quantification 557

of health risks: Global and regional burden of disease attributable to selected major risk 558

factors. Geneva: World Health Organization; 2004. 559

43. Renn O, Walker KD. Global Risk Governance: Concept and Practice Using the 560

IRGC Framework: Springer London, Limited; 2008. 561

44. EFSA Panel on Animal Health and Welfare. Guidance on Risk Assessment for 562

Animal Welfare. EFSA Journal. 2012; 10:1-30. 563

45. Hopkin P. Fundamentals of Risk Management: Understanding, Evaluating and 564

Implementing Effective Risk Management: Kogan Page; 2012. 565

46. National Research Council N. Science and Decisions: Advancing Risk Assessment: 566

The National Academies Press; 2009. 567

47. Rogers DJ, Suk JE, Semenza JC. Using global maps to predict the risk of dengue in 568

Europe. Acta tropica. 2014; 129:1-14. 569

48. Rogers DJ, Sedda L. Statistical models for spatially explicit biological data. 570

Parasitology. 2012; 139:1852-69. 571

49. Gelfand AE, Diggle P, Guttorp P, Fuentes M. Handbook of Spatial Statistics: 572

Taylor & Francis; 2010. 573

50. Clayton D, Kaldor J. Empirical Bayes estimates of age-standardized relative risks 574

for use in disease mapping. Biometrics. 1987:671-81. 575

51. Maantay JA. Geospatial Analysis of Environmental Health: Springer London, 576

Limited; 2011. 577

52. Kitron U. Risk maps: transmission and burden of vector-borne diseases. Parasitol 578

Today. 2000; 16:324-5. 579

53. Keller M, Blench M, Tolentino H, Freifeld CC, Mandl KD, Mawudeku A, et al. Use 580

of unstructured event-based reports for global infectious disease surveillance. Emerg Infect 581

Dis. 2009; 15:689-95. 582

54. Rothman KJ, Greenland S, Lash TL. Modern Epidemiology: Wolters Kluwer 583

Health/Lippincott Williams & Wilkins; 2008. 584

55. Elliott P, Best N. Spatial epidemiology: methods and applications: Oxford 585

University Press; 2001. 586

56. Ghosh M, Natarajan K, Waller LA, Kim D. Hierarchical Bayes GLMs for the 587

analysis of spatial data: An application to disease mapping. Journal of Statistical Planning 588

and Inference. 1999; 75:305-18. 589

57. Elliott P, Wartenberg D. Spatial Epidemiology: Current Approaches and Future 590

Challenges. Environmental Health Perspectives. 2004; 112:998-1006. 591

26

58. Pelosse P, Kribs-Zaleta CM, Ginoux M, Rabinovich JE, Gourbiere S, Menu F. 592

Influence of Vectors' Risk-Spreading Strategies and Environmental Stochasticity on the 593

Epidemiology and Evolution of Vector-Borne Diseases: The Example of Chagas' Disease. 594

PloS one. 2013; 8. 595

59. Fuller D, Troyo A, Alimi T, Beier J. Participatory risk mapping of malaria vector 596

exposure in northern South America using environmental and population data. Applied 597

Geography. 2014; 48:1-7. 598

60. Hill DR. Mapping the risk of yellow Fever infection. Current infectious disease 599

reports. 2012; 14:246-55. 600

61. Jentes ES, Poumerol G, Gershman MD, Hill DR, Lemarchand J, Lewis RF, et al. 601

The revised global yellow fever risk map and recommendations for vaccination, 2010: 602

consensus of the Informal WHO Working Group on Geographic Risk for Yellow Fever. 603

The Lancet Infectious Diseases. 2011; 11:622-32. 604

62. Field VK, Ford LMM, Hill DR. Health information for overseas travel : prevention 605

of illness in travellers from the UK. [Rev. ed.]. ed. London: National Travel Health 606

Network and Centre; 2010. 607

63. Markoff L. Yellow Fever Outbreak in Suda. The NEW ENGLAND JOURNAL of 608

MEDICINE. 2013; 368:689-71. 609

64. Brown L, Medlock J, Murray V. Impact of drought on vector-borne diseases--how 610

does one manage the risk? Public health. 2014; 128:29-37. 611

65. Braks M, van Ginkel R, Wint W, Sedda L, Sprong H. Climate change and public 612

health policy: translating the science. International journal of environmental research and 613

public health. 2014; 11:13-29. 614

66. Eisen L, Wong D, Shelus V, Eisen RJ. What is the risk for exposure to vector-borne 615

pathogens in United States national parks? Journal of medical entomology. 2013; 50:221-616

30. 617

67. Eisen RJ, Eisen L. Spatial modeling of human risk of exposure to vector-borne 618

pathogens based on epidemiological versus arthropod vector data. Journal of medical 619

entomology. 2008; 45:181-92. 620

68. Pohjola MV, Leino O, Kollanus V, Tuomisto JT, Gunnlaugsdottir H, Holm F, et al. 621

State of the art in benefit-risk analysis: environmental health. Food and Chemical 622

Toxicology. 2012; 50:40-55. 623

69. Setbon M, Raude J. Population response to the risk of vector-borne diseases: 624

lessons learned from socio-behavioural research during large-scale outbreaks. Emerging 625

health threats journal. 2009; 2:e6. 626

70. Renn O, Klinke A, van Asselt M. Coping with Complexity, Uncertainty and 627

Ambiguity in Risk Governance: A Synthesis. Ambio. 2011; 40:231-46. 628

71. Palatnik-de-Sousa CB, Day MJ. One Health: the global challenge of epidemic and 629

endemic leishmaniasis. Parasites & vectors. 2011; 4:197. 630

72. Elith J, Leathwick JR. Species Distribution Models: Ecological Explanation and 631

Prediction Across Space and Time. Annual Review of Ecology, Evolution, and 632

Systematics. 2009; 40:677-97. 633

73. Stirling AC, Scoones I. From Risk Assessment to Knowledge Mapping: Science, 634

Precaution, and Participation in Disease Ecology. Ecology & Society. 2009; 14. 635

27

74. Hongoh V, Hoen AG, Aenishaenslin C, Waaub JP, Belanger D, Michel P. Spatially 636

explicit multi-criteria decision analysis for managing vector-borne diseases. International 637

journal of health geographics. 2011; 10:70. 638

75. Reiter P, Thomas CJ, Atkinson PM, Hay SI, Randolph SE, Rogers DJ, et al. Global 639

warming and malaria: a call for accuracy. The Lancet Infectious Diseases. 2004; 4:323-4. 640

76. Suter Ii GW, Norton SB, Barnthouse LW. The Evolution of Frameworks for 641

Ecological Risk Assessment from the Red Book Ancestor. Human and Ecological Risk 642

Assessment: An International Journal. 2003; 9:1349-60. 643

77. European Commission. Regulation (EC) No 178/2002 of the European Parliament 644

and of the Council of 28 January 2002 laying down the general principles and requirements 645

of food law, establishing the European Food Safety Authority and laying down procedures 646

in matters of food safety. Official Journal of the European Communities. 2002; 45:1-24. 647

78. Mindell JS, Boltong A, Forde I. A review of health impact assessment frameworks. 648

Public health. 2008; 122:1177-87. 649

79. World Health Organization W. International Health Regulations (2005): Nonserial 650

Publication: World Health Organization; 2006. 651

80. Ezzati M, Lopez AD, Rodgers AA, Murray CJL. Comparative Quantification of 652

Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk 653

Factors: Organisation mondiale de la santé; 2004. 654

81. European Commission. Regulation (EC) No. 851/2004 of the European Parliament 655

and of the Council of 21 April 2004 establishing a European centre for disease prevention 656

and control. Official Journal of the European Union. 2004; 47:1-11. 657

82. European Commission. White paper: Together for Health: A Strategic Approach for 658

the EU 2008-2013. [white paper]. In press 2007. 659

83. Brunette GW, Kozarsky PE, Magill AJ, Shlim DR, Whatley AD, editors. CDC 660

Health Information for International Travel : The Yellow Book 2012. Oxford: Oxford 661

University Press; 2012. 662

84. Parola P. Eurotravnet. Trop Med Int Health. 2011; 16:51-. 663

85. Team HW. Travax-Travel Health-HPS. 2006. 664

86. Lindsey NP, Brown JA, Kightlinger L, Rosenberg L, Fischer M. State health 665

department perceived utility of and satisfaction with ArboNET, the U.S. National Arboviral 666

Surveillance System. Public Health Rep. 2012; 127:383-90. 667

87. Zeldenrust ME, Rahamat-Langendoen JC, Postma MJ, van Vliet JA. The value of 668

ProMED-mail for the Early Warning Committee in the Netherlands: more specific 669

approach recommended. Euro Surveill. 2008; 13. 670

88. Freifeld CC, Mandl KD, Reis BY, Brownstein JS. HealthMap: global infectious 671

disease monitoring through automated classification and visualization of Internet media 672

reports. Journal of the American Medical Informatics Association : JAMIA. 2008; 15:150-673

7. 674

89. Collier N, Doan S, Kawazoe A, Goodwin RM, Conway M, Tateno Y, et al. 675

BioCaster: detecting public health rumors with a Web-based text mining system. 676

Bioinformatics. 2008; 24:2940-1. 677

90. Tolentino H, Kamadjeu R, Fontelo P, Liu F, Michael Matters M, Marjorie Pollack 678

M, et al. Scanning the Emerging Infectious Diseases Horizon - Visualizing ProMED 679

Emails Using EpiSPIDER. Advances in Disease Surveillance. 2007; 2. 680

28

91. Flahault A, Dias-Ferrao V, Chaberty P, Esteves K, Valleron AJ, Lavanchy D. 681

FluNet as a tool for global monitoring of influenza on the Web. JAMA : the journal of the 682

American Medical Association. 1998; 280:1330-2. 683

92. Welte VR, Vargas Teran M. Emergency Prevention System (EMPRES) for 684

transboundary animal and plant pests and diseases. The EMPRES-livestock: an FAO 685

initiative. Annals of the New York Academy of Sciences. 2004; 1026:19-31. 686

93. Jebara KB, Caceres P, Berlingieri F, Weber-Vintzel L. Ten years' work on the 687

World Organisation for Animal Health (OIE) Worldwide Animal Disease Notification 688

System. Preventive veterinary medicine. 2012; 107:149-59. 689

94. Pelat C, Turbelin C, Bar-Hen A, Flahault A, Valleron A. More diseases tracked by 690

using Google Trends. Emerg Infect Dis. 2009; 15:1327-8. 691

692

693

694

29

Table 1. Main documents defining risk analysis for public health purposes. 695

Year Agency Document

1960-1970 Various US agencies Recommendations, Guidelines, standards 76

1981 The Society for Risk Analysis (SRA) Vision Statement 4

1983 National Research Council of the US

(NRC)

Red Book 22

1995 Food and Agriculture Organisation of

United Nations (FAO)

Risk analysis and food: the experts’ view 23

2002 European Commission Regulation 178/2002 77

2002 World Organisation for Animal Health

(OIE)

Terrestrial Animal Health Code 41

1999-2005 World Health Organisation (WHO) Health Impact Assessment 78; International

Health Regulations 79; Comparative

Quantification of Health Risks 80

2005 European Commission Regulation 851/2004 81

2005 International Risk Governance Council

(IRGC)

White paper on risk governance (towards an

integrative approach) 43.

2007 European Commission White paper on the health strategy for years

2008-2013 82.

2009 National Research Council of the US

(NRC)

Science and Decisions: Advancing Risk

Assessment 46

2012 European Food Safety Authority Guidance on risk assessment for animal

welfare 44

696

697

30

Table 2. Surveillance and risk communication websites with a focus on vector-borne 698

diseases. 699

Organisation Description Website Reference

CDC Travel health. http://wwwnc.cdc.gov/

travel/

83

ECDC Travel health. http://www.istm.org/eu

rotravnet/main.html

84

NaTHNac Travel health. http://www.nathnac.or

g/travel/

62

NHS Travel health. http://www.travax.nhs.

uk/

85

CDC Arbonet is a site for displaying vector-borne

disease maps based solely on recorded cases

of each disease (i.e. not on models). Maps

are updated continuously as new

surveillance data are acquired.

http://www.cdc.gov/ncid

od/dvbid/westnile/USGS

_frame.html

86

ProMed An internet reporting system aimed at

providing early warning of global threats to

human health

http://www.promedmail.

org/.

87

HealthMap An online cartographic resource maintained

by the Computational Epidemiology Group

of the Children's Hospital, Boston, U.S.A.

The site offers real-time surveillance of

emerging health threats around the world. It

uses an algorithm that monitors eleven

sources of news and reports, among them

http://healthmap.org/en/. 88

31

ProMED-mail, Google News, WHO, OIE

and ECDC.

BioCaster Real-time surveillance website that tracks

over 1700 RSS feeds and plots emerging

health events in Google Maps.

http://born.nii.ac.jp/_dev/

.

89

EpiSPIDER Identifies potential outbreaks by screening

news reports and Twitter feeds (only English

texts) with a naive Bayesian classification

service, then refining the output with web-

based text miners.

http://www.epispider.org/

.

90

WHO FluNet website receives influenza

virological data from national laboratories

and WHO regional databases.

http://www.who.int/influ

enza/gisrs_laboratory/flu

net/en/.

91

FAO EMPRES-i is a global animal disease

information system based on a collection of

official (e.g. governmental) and unofficial

(e.g. personal contacts, NGOs) sources and

displayed on Google Maps

http://empres-

i.fao.org/eipws3g/

92

OIE WAHID provide access to the data held by

OIE on past, present and immediate animal

disease notification.

http://www.oie.int/wahis

_2/public/wahid.php/Wa

hidhome/Home

93

Google Google Flu trends uses regional search

engine results relating to influenza to detect

possible outbreaks.

http://www.google.org/fl

utrends/

94

700