key: cord-0428933-j4cilahc authors: Fox, Spencer J; Bellan, Steven E; Perkins, T Alex; Johansson, Michael A; Meyers, Lauren Ancel title: Downgrading disease transmission risk estimates using terminal importations date: 2018-02-15 journal: bioRxiv DOI: 10.1101/265942 sha: c62511fbf2d1168261a6fb1c624795c425fa278d doc_id: 428933 cord_uid: j4cilahc As emerging and re-emerging infectious diseases like dengue, Ebola, chikungunya, and Zika threaten new populations worldwide, officials scramble to assess local severity and transmissibility, with little to no epidemiological history to draw upon. Standard methods for assessing autochthonous (local) transmission risk make either indirect estimates based on ecological suitability or direct estimates only after local cases accumulate. However, an overlooked source of epidemiological data that can meaningfully inform risk assessments prior to outbreak emergence is the absence of transmission by imported cases. Here, we present a method for updating a priori ecological estimates of transmission risk using real-time importation data. We demonstrate our method using Zika importation and transmission data from Texas in 2016, a high-risk region in the southern United States. Our updated risk estimates are lower than previously reported, with only six counties in Texas likely to sustain a Zika epidemic, and consistent with the number of autochthonous cases detected in 2017. Importation events can thereby provide critical, early insight into local transmission risks as infectious diseases expand their global reach. Data 135 We analyzed all ZIKV importations into Texas from January 2016 to September of 2017, 136 including the county and notification date. County-level purchasing power parity (PPP) in US 137 dollars (12); daily temperature data at a 5 km x 5 km resolution for 2016-2017 and historical 138 averages from 1960-1990 (13,14) were also used as inputs to the transmission risk model. For 139 each county and month, we averaged daily temperatures across all 5 km x 5 km grid cells 140 whose center fell within the county; we aggregated 5 km x 5 km mosquito (Aedes aegypti) 141 occurrence probabilities similarly (15) . Data available doi:10.18738/T8/HYZ53B. 142 In all, six mosquito-borne, autochthonous cases of ZIKV were reported in Texas in 2016 143 and two were reported in 2017 (25). For updating R 0 estimates, we analyzed 2016 data and 144 assumed that two autochthonous cases were detected in Cameron County--one in November 145 and one in December 2016; we excluded four nearby cases discovered during the November 146 follow-up investigation, because our model does not incorporate active surveillance. As 147 sensitivity analyses, we re-estimated R 0 assuming that no cases were detected and that all six 148 cases were detected ( Fig S7) . For validating our estimates, we analyzed 2017 data and 149 considered only one of the two reported autochthonous cases, as the second case occurred 150 outside the timeline of our 2017 importation data. 151 152 A priori county-month R 0 estimates 153 Following Perkins et al (6), we estimated R 0 using the Ross-Macdonald temperature-154 dependent formulation: 155 , 156 with parameters as defined in Table 1 . We calculated relative abundance of the ZIKV vector 157 based on Ae. aegypti occurrence probabilities as -ln(1-occurrence probability), and interpret this 158 as a relative (rather than absolute) abundance, which is sufficient for our R 0 estimation (6). We 159 derived a priori county-month R 0 distributions by drawing 1,000 Monte Carlo samples from each 160 underlying parameter distribution, with the appropriate county and month data. Finally, we fit 161 gamma distributions to each probability distribution for use as an informative priors. 162 163 where (n) = (n-1)!. However, not all cases are detected and the imported index case is always 175 detected and correctly classified as an importation, so the probability of detecting a chain of 176 size, j, from a given importation is given by: 177 178 , 179 where p d is the case detection probability. Importantly, this allows for local, undetected cases. 180 We take the product of all likelihoods for each imported case as 181 , 182 where the vector, O, contains the observed outbreak sizes for each importation (terminal 183 importations have an outbreak size of one), denotes the county ( )-month ( ) R 0 for the 184 location and time that the importation occurred, and α is a statewide scaling factor applied to 185 each . The introduction of the state-wide scaling factor allows for localized importations to 186 inform statewide estimates, but assumes that biases in the a priori R 0 estimation procedure are 187 constant across counties and months. Details of simulations and validation of the likelihood can 188 be found in supplemental section I ( Fig S1) . Estimating the dispersion parameter 191 The negative binomial dispersion parameter governs the variability in secondary cases 192 following each importation, with values near zero meaning that most importations yield few or no 193 cases while a few "superspreaders" produce many. We assume that ZIKV secondary case Importation-based updates of transmission risk 232 Hypothetically, suppose that the first 15 imported cases of Zika into Texas arrived in 233 August into Harris County (which contains Houston) without any detected autochthonous 234 transmission. Prior to these importations, environmental suitability models yielded a relatively 235 high local risk estimate with median Harris county R 0 above the epidemic threshold of one 236 ( Figure 1A -dark grey). The lack of secondary cases following all 15 importations suggests that 237 R 0 may be lower. Indeed, our updated estimates suggest that the Harris county R 0 is likely 238 below one ( Figure 1A -light grey). Our method leverages such county-level importation data to 239 update R 0 estimates throughout the state (via a scaling factor), based on the assumption that 240 any a priori biases will be similar across counties ( Figure 1B ). 241 242 Baseline importation and transmission risks in Texas 243 Prior to making importation-based updates, our initial median estimates of R 0 across 244 Texas' 254 counties in 2016 range from approximately 0 to 1.5 throughout the year with July 245 and August having the highest transmission risk (Figure 2A ). Throughout the manuscript, we 246 conduct a one-sided test at a 1% significance level and thus consider counties with 99 247 percentiles (upper bounds) that include one to be at risk for an epidemic (R 0 > 1). Initial upper 248 bound estimates reach as high as three, and 119 (47%) of Texas counties are expected to be at 249 risk of a local outbreak in at least one month of the year (Figure 2A, S2) . When we considered 250 historic average temperatures rather than 2016 temperatures, the projected 2017 risks were 251 consistently lower, with the largest differences occurring during the unseasonably warm 2017 252 winter (Fig S4) . Based on all importations and autochthonous cases that occurred in Texas prior to 273 January 2017, we estimate that all Texas counties have a median posterior R 0 below one (Fig 274 3) . Median estimates range from 0 to 0.29; upper-bound estimates range from 0 to 1.12, with 275 only six (5%) of the original 119 high-risk counties maintaining epidemic potential (Fig S5) . 276 When we assume historic averages rather than 2016 temperatures, we obtain similar results 277 (Fig S6) . 278 In a sensitivity analysis that assumes ~20 times more undetected importations, we found 279 that the estimated risks decreased further (Fig S7) . We also varied the number of detected 280 autochthonous cases in November: as they decrease from one to zero, the estimated risks 281 decrease considerably; as they increase to five, estimated risks increase, with 83 counties 282 becoming at risk for a local outbreak (Fig S7) . 283 Importation events had variable impacts on the posterior estimates, depending on their 289 timing and location (Fig 4) . The global expansion of ZIKV was declared a Public Health Emergency of International 331 Concern in February 2016, and caused more than 565,000 confirmed or probable cases and 332 over 3,352 documented cases of congenital Zika syndrome. Although it is receding in most 333 regions of the world, ecological risk assessments suggest that previously unaffected or 334 minimally affected areas may remain at risk for future emergence, including parts of Asia and 335 South America (26) (27) (28) . Differentiating regions that can sustain a ZIKV epidemic (R 0 >1) from 336 those that cannot is vital to effective planning and resource allocation for future preparedness 337 plans. To address this challenge, we have developed a simple method for refining uncertain risk 338 assessments with readily available data on disease importations. 339 We applied the method to update ZIKV R 0 estimates for each of the 254 counties in 340 Texas, and found that only six counties have non-negligible probabilities of sustained local 341 transmission. This is a substantial downgrade in expected risk, given that 43% of the 254 342 counties were previously thought to be vulnerable to ZIKV outbreaks. These estimates suggest 343 that there should have been roughly one detected case of locally acquired ZIKV between 344 January and September of 2017, closely corresponding to the single transmission event actually 345 detected in Cameron County in July 2017 ( Figure 5 ). Our sensitivity analysis suggests that, if we 346 underestimated case-reporting in November, 77 additional counties have non-negligible but low 347 risks of summer outbreaks. Given comparable importation and climatic data, this approach 348 e sk lly e w could readily update ZIKV transmission risk estimates for all counties in the continental US and 349 elsewhere. 350 Our estimation method relies on several simplifying assumptions. We assumed that the 351 shape of the secondary case distribution resembles that of dengue. Although we have no 352 evidence to the contrary, this should be updated as ZIKV-specific estimates become available 353 (23). We also assumed that transmission is equally likely from imported and locally acquired 354 cases. Imported cases may be less infectious than locally acquired cases for two reasons, 355 leading us to underestimate local transmission risks. First, they may be more likely to receive 356 care or education that limits subsequent transmission, although most ZIKV cases are inapparent 357 or mild, and do not require medical care (11); second, if they arrive already infectious, their local 358 infectious periods may be shorter than those of autochthonous cases. Next, we treat all 359 importations as independent. However, spatiotemporal heterogeneity in case detection 360 probabilities or clustering of cases (e.g., testing of travel companions) could bias risk estimates. 361 Furthermore, when secondary clusters are detected, we assume they share a transmission tree 362 stemming from a single detected importation. In fact, the low ZIKV detection rate suggests that 363 both primary importations and secondary cases are likely to be missed. If the detection rates are 364 roughly similar, our results hold. When we assume, in sensitivity analysis, that importations are 365 detected at higher rates than secondary cases, then the resulting risk estimates will be higher; 366 when we assume the reverse, they are lower. The additional assumption, that clusters are 367 epidemiologically connected, seems reasonable for the small contained outbreaks detected in 368 Texas, but may not be appropriate for importation-fueled arbovirus outbreaks in Florida, for 369 example. In such cases, molecular data might enable estimation of transmission clusters 370 (32,33). We also rely on informative Bayesian priors and a statewide scaling factor, which 371 allows us to use local importations to inform risk estimates elsewhere, but implies that our prior 372 county-month transmission risk estimates are correct relative to each other. Given additional 373 importation data, we could potentially estimate each county-month R 0 independently. Finally, we 374 do not consider possibility of sexual transmission of ZIKV. While sexual transmission has 375 occurred and may be important for specific populations (29), we assumed that mosquito-borne 376 transmission is the dominant mode of infection. 377 During the height of the ZIKV threat, public health agencies in the US rapidly 378 implemented both preventative measures (e.g., vector control and educational campaigns) and 379 response measures (e.g. laboratory testing and epidemic trigger plans), particularly in high risk 380 southern states. Decision makers sought to identify and narrow the spatiotemporal scope of 381 outbreak risk to enable targeted responses, efficiently allocate resources, and avoid false 382 alarms (10,30). Our method facilitates such rapid, real-time geographic risk estimation from 383 typical early outbreak data, and suggests that only 3% of the Texas population is at risk for a 384 local outbreak. Critically, we can conclude neither that all initial ecological risk assessments for 385 ZIKV will overestimate risk, although this seems to be the case for ZIKV in Texas, nor that 386 public health preparations and interventions for ZIKV are no longer necessary in Texas or the 387 southern US. Rather, our results suggest that sustained ZIKV outbreaks are unlikely, but not 388 impossible, and provide more robust and localized estimates of ZIKV risk that can inform more 389 targeted surveillance and reactions to future ZIKV importations. 390 This framework can be applied to update any R 0 estimates using importation data, 391 regardless of the a priori method of estimation. 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