Skip to main content
NCBI home page
GeoHealth logoLink to GeoHealth
. 2023 Apr 4;7(4):e2022GH000747. doi: 10.1029/2022GH000747

Patterns of West Nile Virus in the Northeastern United States Using Negative Binomial and Mechanistic Trait‐Based Models

Alexander C Keyel 1,2,✉
PMCID: PMC10072317  PMID: 37026081

Abstract

West Nile virus (WNV) primarily infects birds and mosquitoes but has also caused over 2,000 human deaths, and >50,000 reported human cases in the United States. Expected numbers of WNV neuroinvasive cases for the present were described for the Northeastern United States, using a negative binomial model. Changes in temperature‐based suitability for WNV due to climate change were examined for the next decade using a temperature‐trait model. WNV suitability was generally expected to increase over the next decade due to changes in temperature, but the changes in suitability were generally small. Many, but not all, populous counties in the northeast are already near peak suitability. Several years in a row of low case numbers is consistent with a negative binomial, and should not be interpreted as a change in disease dynamics. Public health budgets need to be prepared for the expected infrequent years with higher‐than‐average cases. Low‐population counties that have not yet had a case are expected to have similar probabilities of having a new case as nearby low‐population counties with cases, as these absences are consistent with a single statistical distribution and random chance.

Keywords: Arbovirus, vector‐borne disease, negative binomial, temperature‐trait model, climate change, public health

Key Points

  • Small increases in temperature‐based suitability for West Nile virus (WNV) are expected over most of the northeastern US in the next decade

  • Years with high and low cases are consistent with the same underlying statistical distribution

  • Temperature‐based suitability for WNV in some of the most populous counties is expected to decrease under climate change

1. Introduction

Climate change is predicted to adversely affect human health and economic productivity (USGCRP, 2018, p. 18). One way climate change is expected to affect human health is through changes to patterns of infectious disease (e.g., Ryan et al., 2015). West Nile virus (WNV) is a vector‐borne disease of public health concern (Hayes et al., 2005; Keyel, Gorris, et al., 2021) and is expected to change its distribution due to climate change (Chen et al., 2013; Hoover & Barker, 2016; Keyel, Raghavendra, et al., 2021). Broadly, WNV is expected to shift northward, but regional temperature‐based analyses show that changes may vary depending on regional differences in temperature (Keyel, Raghavendra, et al., 2021; Morin & Comrie, 2013). For most of the Northeast, temperatures are predicted to warm, especially minimum (night‐time) temperatures (Liu et al., 2017). Precipitation is also predicted to increase, especially in winter, due in part to an increased number of storms (Lynch et al., 2016; Thibeault & Seth, 2014). Summers may see increased run‐off and periods of dryness (Lynch et al., 2016). In the next 10 years, the climate is expected to warm by 0.2–0.5°C (Liu et al., 2017). These predicted changes are within the range of variation in temperature currently experienced (Figure 1).

Figure 1.

Figure 1

Open in a new tab

(a) Present‐day mean temperature for July–September, (b) standard deviation of present‐day temperature. Map base layer from 2017 TIGER/Shapefiles (US Census Bureau, 2021).

Probabilistic null models have been previously developed for the United States (Keyel & Kilpatrick, 2021). These models consider a range of possible outcomes, rather than predicting one single number of cases for the future. In one instance, a model that worked well in a non‐probabilistic context (e.g., Keyel et al., 2019) was not able to outperform a probabilistic negative binomial null model in a predictive context. A negative binomial was also shown to be among the best models in a national WNV forecasting challenge (Holcomb et al., 2023). As a consequence, current models for the Northeastern US are very good at describing the range of possible outcomes, but do not provide much information on where in the range of outcomes a particular year will fall.

Mosquito‐temperature‐trait models have been productively used to understand the potential for vector‐borne diseases to respond to climate change (Mordecai et al., 2019; Ryan et al., 2015). They were recently adapted to WNV (Shocket et al., 2020). Statistical models trained on human cases that included an adequate temperature range largely supported the results of mosquito‐trait‐based models for WNV in New York and Connecticut (Keyel, Raghavendra, et al., 2021).

These two approaches serve as complementary measures of WNV risk. One describes the probability distribution for numbers of human neuroinvasive cases, while the other examines the suitability of temperatures for WNV and a key vector species. These measures were used to describe present day risk and examine the potential for present day risks to change in the next decade due to climate change. These measures of risk provide insights into short‐term adaptation measures that can be taken.

2. Methods

2.1. Negative Binomial Model

Negative binomial model predictions were taken from Keyel and Kilpatrick (2021), as that was found to be among the strongest null models in the Northeast. The negative binomial was chosen instead of the historical null model due to the long time series, and the capacity to downscale model results based on population (Klenke, 2008). The model used here was implemented in R (R Core Team, 2017). This distribution allowed us to cleanly calculate probability of an arbitrary number of human neuroinvasive cases for an arbitrary number of years into the future. One downside of the negative binomial model is it does not predict whether a particular year will have cases or not, but it can give insights into the overall probability of a given number of WNV neuroinvasive cases.

A negative binomial was fit to each county individually, and to groups of multiple counties (Figure S1). Group assignment was subjective and followed the following guidelines: Each group needed to be contiguous with surrounding counties, contain counties from only one state, and include a minimum of 6,00,000 people in each group. No upper group size was imposed, but groups with more than 1,200,000 were examined to see if they could be split into two or more groups. Secondarily, counties with similar population density were preferentially grouped together. The probability a county would have zero WNV cases by chance was calculated, assuming the down‐scaled group negative binomial distribution was true. Future research could examine alternative spatial methods for aggregating counties in order to improve risk estimates.

2.2. Climate Change Predictions

This manuscript builds upon this prior work by expanding the use of the mosquito temperature‐trait models to the entire northeast. Briefly, these models use mosquito life history traits to estimate a relative temperature‐based WNV suitability (relative R 0). Equation 1 incorporates traits related to WNV incubation PDR(T), dissemination and transmission vc(T), and with traits related to mosquito life history relating to frequency of biting a(T), daily mortality rate, u(T), egg production per female per gonotrophic cycle, EFGC(T), egg viability, EV(T), larval survival, pLA(T), and mosquito development rate MDR(T). The virus‐related terms of the equation relate to how fast and effectively the virus can be transmitted, while the mosquito‐related terms define how fast the mosquito population is capable of growing. Traits are combined in a multiplicative framework (see Equation 1, modified from Shocket et al. (2020), see (Keyel, Raghavendra, et al., 2021) for more details) to give R 0(T). R 0(T) is then scaled to give an index of relative suitability between 0 and 1.

R0(T)=a(T)3∗vc(T)∗e−u(T)PDR(T)∗EFGC(T)∗EV(T)∗pLA(T)∗MDR(T)∗u(T)−3 (1)

Host density and disease duration were assumed to be constant and were omitted from the equation because they cancel out during the index scaling (but see Kilpatrick et al., 2006 for the potential for host heterogeneity to affect these results). Critically, these models look at the contribution of temperature only to risk, other important factors such as breeding habitat availability, land cover (Bradley et al., 2008), or precipitation are not considered (Shocket et al., 2020). These models were developed in the context of Culex pipiens (Shocket et al., 2020). Cx. pipiens is one of the most important mosquito vectors for WNV across the Northeastern US (Andreadis, 2012; Kilpatrick et al., 2005; Simpson et al., 2012; Turell et al., 2005). Cx. pipiens is closely related to, and hybridizes with Cx. quinquefasciatus (Farajollahi et al., 2011), an important WNV vector in the Southeastern US (Godsey et al., 2005). Relative risk estimates for Cx. pipiens and Cx. quinquefasciatus are similar (see Shocket et al., 2020, Figure 7), especially with respect to the optimum and upper end of the curve. Consequently, these methods are expected to remain valid even if the distributions of these two species shift in the future. Present day conditions and risk expected for 0.5°C warming were examined. Present‐day July–September mean temperatures were derived from GridMET (Abatzoglou, 2013), using the GridMET downloader tool (Wimberly & Davis, 2019) and averaged over the entire period. Model results up to 4°C warming were generated (see Table S2 in Keyel, 2022a). Up to 4°C warming for the 5 most densely populated counties in the Northeast was also examined (Table 1). Warming of up to 4°C is within the realm of possible temperature changes for the Northeast by the end of the century (IPCC, 2014; Liu et al., 2017). This is also the rationale for 0.5°C for an upper‐bound for the increase in temperature in the next decade (4°C/8 decades = 0.5°C per decade). Note that the analyses here calculated trait‐based risk on mean temperatures for the region. Due to the non‐linear response curve, the quantitative results would have differed if relative risk were calculated first, and then averaged. A second source of error for this approach is microclimatic variability, which can affect disease risk (Haider et al., 2017). Third, timing of mosquito activity may allow mosquitoes to buffer against extreme heat and cold (Danforth et al., 2016), thus there is a mismatch between temperature means at a weather station and the temperatures experienced by the mosquitoes. Future research could refine these results, but the broad patterns are expected to be qualitatively similar.

Table 1.

Relative R 0's for 5 Major Northeastern US Metropolitan Counties Based on Different Levels of Warming (in C)

County +0.0 +0.5 +1.0 +1.5 +2.0 +2.5 +3.0 +3.5 +4.0
Suffolk (Boston) 0.88 0.92 0.96 0.99 1.00 1.00 0.98 0.96 0.92
New York (Manhattan) 1.00 1.00 0.98 0.95 0.91 0.86 0.80 0.73 0.66
Philadelphia 1.00 1.00 0.99 0.96 0.92 0.88 0.82 0.75 0.68
Baltimore City 0.98 0.95 0.91 0.86 0.80 0.74 0.66 0.58 0.50
District of Columbia 1.00 0.99 0.96 0.92 0.87 0.82 0.75 0.67 0.597
Open in a new tab

3. Results

Most counties (374 of 433) in the study region had a low probability (<20% chance) of having a single WNV neuroinvasive case in the next year (Figure 2a). Greater than three times as many counties had a high probability of having at least one neuroinvasive case (>60% chance) in a 5‐year time frame (63, Figure 2b) compared to a 1‐year timeframe (17 of 433, Figure 2a). Relatively few counties (10 of 433) had a high probability (>60% chance) of having a year with at least 5 neuroinvasive cases within a 5‐year timeframe, and these were predominantly urban areas with high populations (Figure 2c). Two groups, containing 40 counties, never had a single reported case of WNV. For the remaining groups with WNV cases, counties with no observed cases were predicted to have a low probability of cases based on the group model (see Table S1 in Keyel, 2022a). Only one county (Washington, PA) had a <0.05 probability of having 20 years of no cases by chance assuming a similar risk to the rest of the counties in the group (Table S1 in Keyel, 2022a). When corrected for multiple comparisons, no county differed significantly from the negative binomial model (with 174 comparisons, ∼8–9 counties would be expected to have a p value < 0.05 by chance (Table S1 in Keyel, 2022a)).

Figure 2.

Figure 2

Open in a new tab

(a) Probability of having one case of West Nile virus (WNV) in the next year, (b) probability of having at least one case of WNV in the next 5 years, and (c) probability of having 1 year with at least 5 cases in the next 5 years. The negative binomial model was fit based on historical cases. Counties with less than 600,000 people were merged with other contiguous counties in the same state until at least a 600,000 person threshold was reached, to ensure a sufficient population size to detect WNV at low incidence. Results were then downscaled back to the county level. Underlying spatial data from 2017 TIGER/Shapefiles from US Census bureau (US Census Bureau, 2021).

When temperature‐based suitability was divided up into 5 equal intervals, 223 counties had the highest temperature‐based suitability for WNV, while 14 were in the lowest suitability category (Figure 3a). Most counties (84%, 365 of 433) were predicted to increase in temperature‐based suitability over the next decade (Figure 3b).

Figure 3.

Figure 3

Open in a new tab

(a) Present‐day temperature‐trait‐predicted relative risk for Culex pipiens, and (b) the predicted change in temperature‐based risk with 0.5°C warming (on the high end for predicted for warming in the next decade). Note that this amount of warming falls within the range of present day temperature variation around the mean presented in (a). Map base layer from 2017 TIGER/Shapefiles (US Census Bureau, 2021).

The five most densely populated urban areas are expected to remain in a suitable temperature range for WNV under future warming (Table 1). Once 0.5°C of warming has occurred, 4 of the 5 urban areas will decrease in temperature‐based suitability with further warming. However, substantial reductions in suitability for many major urban areas will not occur with less than 2.5°C of warming.

4. Discussion

A negative binomial distribution provides a simple method for describing patterns of WNV in the northeast (Keyel & Kilpatrick, 2021) and was among the top models in a national WNV forecasting challenge (Holcomb et al., 2023). The most important insight for public health is that a series of years with no or few WNV cases is possible even with a constant probability distribution for WNV cases. This means that reducing public health expenditures based on a few years with low WNV, on the assumption that it “has gone awayˮ is a poor strategy and will leave public health unprepared for the expected high years. WNV budgets should consider probabilities of WNV cases over at least 5‐year time horizons and have an emergency fund or the capacity to roll over funds from 1 year to the next, in order to address the expected high WNV years.

Further, areas with low rates of WNV may want to adopt a regional response approach that ensures counties have access to resources when cases occur. For most of the Northeast, a previous absence of a neuroinvasive WNV case over the past 20 years is not an indicator that the county will remain free of neuroinvasive WNV cases in the future, or even is lower risk than counties that have previously had cases. This suggests that these counties simply did not have cases due to low populations and random chance. The exceptions to this are in most of Maine (Figure 2) and southwestern Virginia, where no cases have been reported. Both of these areas are predicted to have increased temperature‐based suitability in the next decade due to climate change (Figure 3). This is likely to be more relevant for Maine than Virginia, as current temperature‐based suitability is relatively low in Maine. In Virginia, temperature‐based suitability is already high, suggesting some other factor is responsible for the reduced number of cases. Therefore, southwestern Virginia may not see an increase in number of cases due to warming.

In the long‐run, temperature‐based suitability for WNV is expected to increase across most of the Northeast, with the largest increases predicted in areas with relatively low present‐day suitability. Decreases in suitability are predicted for the southern portion of the region. Locations where WNV is relatively rare will need to be on the look‐out for an increase in cases (Figure 3b). These counties can expect to see substantial increases in temperature‐based suitability in the coming decades. Some of these regions should prepare to begin surveillance programs, doctors should familiarize themselves with WNV symptoms and lab work, and mosquito control operations should be prepared for expanded operations to reduce disease risk. That said, temperature‐based suitability is currently high in some localities that have low numbers of observed WNV neuroinvasive cases, and therefore other factors may also be critical in determining how numbers of WNV cases may change into the future. For example, land cover (Rochlin et al., 2008), mosquito control efforts (Bellini et al., 2014), mosquito microbiota (Novakova et al., 2017), population density (Rochlin et al., 2008), demographic structure of the human populations (Ruiz et al., 2004), socio‐economic status (Rothman et al., 2021), presence of septic systems (Myer et al., 2017), synchrony with avian breeding (Caillouët et al., 2013), among other factors influence WNV dynamics and could change WNV case numbers.

Locations with the most WNV cases in the present will have relatively little to do for long‐term climate‐change‐related planning for WNV. Existing mitigation measures should be as effective or more effective at controlling WNV in the future, as conditions shift to be less suitable for mosquito‐based transmission of WNV. This rests on the assumption of no evolution. However, temperature‐dependent evolution of the WNV has been shown in the lab (Fay et al., 2021) and in the field (Bialosuknia et al., 2022). A further caveat is this research was purely from the standpoint of WNV. In locations where WNV is expected to decline, other vector‐borne diseases, such as Zika virus, and the vectors that spread them, such as Aedes aegypti, may expand (Kraemer et al., 2019; Ryan et al., 2021). However, warming is not predicted to be sufficient to be suitable for dengue to become endemic in the Northeast under future climate change scenarios that extend out to 2080 (Messina et al., 2019).

The mismatch between probabilistic present‐day probabilities of human cases compared to temperature‐trait‐based suitability for the Northeast is interesting (compare Figures 2b and 3a). Virginia and West Virginia have lower probabilities of human cases than expected based on the temperature suitability models, while western and central Pennsylvania appears to have higher probabilities of cases than predicted by the temperature suitability models. Future work can explore whether landcover can explain these discrepancies, as prior research has suggested that urban areas are more favorable to WNV amplification (Bradley et al., 2008).

Another interesting future direction would be to compare the probabilities of human neuroinvasive cases with present‐day surveillance effort. Are some regions under‐surveyed for vector‐borne diseases? Could additional surveillance reduce human cases in these regions? Do some areas with high surveillance have fewer cases than predicted? Are they over‐surveyed, or does the enhanced surveillance lead to fewer cases?

Conflict of Interest

The authors declare no conflicts of interest relevant to this study.

Supporting information

Figure S1

Click here for additional data file. (203.7KB, docx)

Acknowledgments

I thank A. Tyre for statistical assistance and O. Elison Timm for feedback on an earlier draft of the manuscript. I thank S. Mathis and the Centers for Disease Control for access to the CDC neuroinvasive data. This publication was supported by cooperative agreement 1U01CK000509‐01, funded by the Centers for Disease Control and Prevention and by the National Institutes of Health Grant R01AI168097. Its contents are solely the responsibility of the author and do not necessarily represent the official views of the Centers for Disease Control and Prevention or the Department of Health and Human Services.

Keyel, A. C. (2023). Patterns of West Nile virus in the Northeastern United States using negative binomial and mechanistic trait‐based models. GeoHealth, 7, e2022GH000747. 10.1029/2022GH000747

Data Availability Statement

Neuroinvasive case data are available from the Centers for Disease Control Division of Vector‐borne Diseases upon request (dvbid2@cdc.gov), subject to a data use agreement. Maps were created using ArcGIS 10.6.1 (Redlands, CA) with 2017 US Census TIGER/Shapefiles as the county base layer (US Census Bureau, 2021). Population data were derived from US Census data (US Census Bureau, 2017, 2019). Code used to run the analysis is available from Zenodo (Keyel, 2022b). Data tables with expanded results are also available from Zenodo (Keyel, 2022a).

References

  1. Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131. 10.1002/joc.3413 [DOI] [Google Scholar]
  2. Andreadis, T. G. (2012). The contribution of Culex pipiens complex mosquitoes to transmission and persistence of West Nile virus in North America. Journal of the American Mosquito Control Association, 28(4s), 137–151. 10.2987/8756-971x-28.4s.137 [DOI] [PubMed] [Google Scholar]
  3. Bellini, R. , Zeller, H. , & Van Bortel, W. (2014). A review of the vector management methods to prevent and control outbreaks of West Nile virus infection and the challenge for Europe. Parasites & Vectors, 7(1), 323. 10.1186/1756-3305-7-323 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bialosuknia, S. M. , Dupuis Ii, A. P. , Zink, S. D. , Koetzner, C. A. , Maffei, J. G. , Owen, J. C. , et al. (2022). Adaptive evolution of West Nile virus facilitated increased transmissibility and prevalence in New York state. Emerging Microbes & Infections, 11(1), 988–999. 10.1080/22221751.2022.2056521 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bradley, C. A. , Gibbs, S. E. J. , & Altizer, S. (2008). Urban land use predicts West Nile virus exposure in songbirds. Ecological Applications, 18(5), 1083–1092. 10.1890/07-0822.1 [DOI] [PubMed] [Google Scholar]
  6. Caillouët, K. A. , Riggan, A. E. , Bulluck, L. P. , Carlson, J. C. , & Sabo, R. T. (2013). Nesting bird “host funnel” increases mosquito‐bird contact rate. Journal of Medical Entomology, 50(2), 462–466. 10.1603/me12183 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chen, C. C. , Jenkins, E. , Epp, T. , Waldner, C. , Curry, P. S. , & Soos, C. (2013). Climate change and West Nile virus in a highly endemic region of North America. International Journal of Environmental Research and Public Health, 10(7), 3052–3071. 10.3390/ijerph10073052 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Danforth, M. E. , Reisen, W. K. , & Barker, C. M. (2016). The impact of cycling temperature on the transmission of West Nile virus. Journal of Medical Entomology, 53(3), 681–686. 10.1093/jme/tjw013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Farajollahi, A. , Fonseca, D. M. , Kramer, L. D. , & Kilpatrick, A. M. (2011). “Bird biting” mosquitoes and human disease: A review of the role of Culex pipiens complex mosquitoes in epidemiology. Infection, Genetics and Evolution, 11(7), 1577–1585. 10.1016/j.meegid.2011.08.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Fay, R. L. , Ngo, K. A. , Kuo, L. , Willsey, G. G. , Kramer, L. D. , & Ciota, A. T. (2021). Experimental evolution of West Nile virus at higher temperatures facilitates broad adaptation and increased genetic diversity. Viruses, 13(10), 1889. 10.3390/v13101889 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Godsey, M. S. J. , Nasci, R. , Savage, H. M. , Aspen, S. , King, R. , Powers, A. M. , et al. (2005). West Nile virus‐infected mosquitoes, Louisiana, 2002. Emerging Infectious Diseases, 11(9), 1399–1404. 10.3201/eid1109.040443 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Haider, N. , Kirkeby, C. , Kristensen, B. , Kjaer, L. J. , Sorensen, J. H. , & Bodker, R. (2017). Microclimatic temperatures increase the potential for vector‐borne disease transmission in the Scandinavian climate. Scientific Reports, 7(1), 8175. 10.1038/s41598-017-08514-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Hayes, E. B. , Sejvar, J. J. , Zaki, S. R. , Lanciotti, R. S. , Bode, A. V. , & Campbell, G. L. (2005). Virology, pathology, and clinical manifestations of West Nile virus disease. Emerging Infectious Diseases, 11(8), 1174–1179. 10.3201/eid1108.050289b [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Holcomb, K. M. , Mathis, S. , Staples, J. E. , Fischer, M. , Barker, C. M. , Beard, C. B. , et al. (2023). Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction. Parasites & Vectors, 16(1), 11. 10.1186/s13071-022-05630-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hoover, K. C. , & Barker, C. M. (2016). West Nile virus, climate change, and circumpolar vulnerability. Wiley Interdisciplinary Reviews: Climate Change, 7(2), 283–300. 10.1002/wcc.382 [DOI] [Google Scholar]
  16. IPCC . (2014). In Pachauri R. K. & Meyer L. A. (Eds.), Climate change 2014: Synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change [Core writing team] (p. 151). IPCC. [Google Scholar]
  17. Keyel, A. C. (2022a). Supplemental data for “Patterns of West Nile virus in the northeastern United States using negative binomial and mechanistic trait‐based models”. [Dataset]. Zenodo. 10.5281/zenodo.7311872 [DOI] [PMC free article] [PubMed]
  18. Keyel, A. C. (2022b). Code for “Patterns of West Nile virus in the northeastern United States using negative binomial and mechanistic trait‐based models”. Zenodo. 10.5281/zenodo.7311878 [DOI] [PMC free article] [PubMed]
  19. Keyel, A. C. , Elison Timm, O. , Backenson, P. B. , Prussing, C. , Quinones, S. , McDonough, K. A. , et al. (2019). Seasonal temperatures and hydrological conditions improve the prediction of West Nile virus infection rates in Culex mosquitoes and human case counts in New York and Connecticut. PLoS One, 14(6), e0217854. 10.1371/journal.pone.0217854 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Keyel, A. C. , Gorris, M. E. , Rochlin, I. , Uelmen, J. A. , Chaves, L. F. , Hamer, G. L. , et al. (2021). A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision‐making. PLoS Neglected Tropical Diseases, 15(9), e0009653. 10.1371/journal.pntd.0009653 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Keyel, A. C. , & Kilpatrick, A. M. (2021). Probabilistic evaluation of null models for West Nile virus in the United States. BioRxiv. 10.1101/2021.07.26.453866 [DOI] [Google Scholar]
  22. Keyel, A. C. , Raghavendra, A. , Ciota, A. T. , & Elison Timm, O. (2021). West Nile virus is predicted to be more geographically widespread in New York state and Connecticut under future climate change. Global Change Biology, 27(21), 5430–5445. 10.1111/gcb.15842 [DOI] [PubMed] [Google Scholar]
  23. Kilpatrick, A. M. , Daszak, P. , Jones, M. J. , Marra, P. P. , & Kramer, L. D. (2006). Host heterogeneity dominates West Nile virus transmission. Proceedings of the Royal Society of London B Biological Sciences, 273(1599), 2327–2333. 10.1098/rspb.2006.3575 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kilpatrick, A. M. , Kramer, L. D. , Campbell, S. R. , Alleyne, E. O. , Dobson, A. P. , & Daszak, P. (2005). West Nile virus risk assessment and the bridge vector paradigm. Emerging Infectious Diseases, 11(3), 425–429. 10.3201/eid1103.040364 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Klenke, A. (2008). Probability theory: A comprehensive course. Springer‐Verlag. [Google Scholar]
  26. Kraemer, M. U. G. , Reiner, R. C. , Brady, O. J. , Messina, J. P. , Gilbert, M. , Pigott, D. M. , et al. (2019). Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nature Microbiology, 4(5), 854–863. 10.1038/s41564-019-0376-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Liu, C. , Ikeda, K. , Rasmussen, R. , Barlage, M. , Newman, A. J. , Prein, A. F. , et al. (2017). Continental‐scale convection‐permitting modeling of the current and future climate of North America. Climate Dynamics, 49(1–2), 71–95. 10.1007/s00382-016-3327-9 [DOI] [Google Scholar]
  28. Lynch, C. , Seth, A. , & Thibeault, J. (2016). Recent and projected annual cycles of temperature and precipitation in the northeast United States from CMIP5. Journal of Climate, 29(1), 347–365. 10.1175/JCLI-D-14-00781.1 [DOI] [Google Scholar]
  29. Messina, J. P. , Brady, O. J. , Golding, N. , Kraemer, M. U. G. , Wint, G. R. W. , Ray, S. E. , et al. (2019). The current and future global distribution and population at risk of dengue. Nature Microbiology, 4(9), 1508–1515. 10.1038/s41564-019-0476-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Mordecai, E. A. , Caldwell, J. M. , Grossman, M. K. , Lippi, C. A. , Johnson, L. R. , Neira, M. , et al. (2019). Thermal biology of mosquito‐borne disease. Ecology Letters, 22(10), 1690–1708. 10.1111/ele.13335 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Morin, C. W. , & Comrie, A. C. (2013). Regional and seasonal response of a West Nile virus vector to climate change. Proceedings of the National Academy of Sciences of the United States of America, 110(39), 15620–15625. 10.1073/pnas.1307135110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Myer, M. H. , Campbell, S. R. , & Johnston, J. M. (2017). Spatiotemporal modeling of ecological and sociological predictors of West Nile virus in Suffolk County, NY, mosquitoes. Ecosphere, 8(6), e01854. 10.1002/ecs2.1854 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Novakova, E. , Woodhams, D. C. , Rodriguez‐Ruano, S. M. , Brucker, R. M. , Leff, J. W. , Maharaj, A. , et al. (2017). Mosquito microbiome dynamics, a background for prevalence and seasonality of West Nile virus. Frontiers in Microbiology, 8. 10.3389/fmicb.2017.00526 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. R Core Team . (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
  35. Rochlin, I. , Harding, K. , Ginsberg, H. , & Campbell, S. (2008). Comparative analysis of distribution and abundance of West Nile and eastern equine encephalomyelitis virus vectors in Suffolk County, New York, using human population density and land use/cover data. Journal of Medical Entomology, 45(3), 563–571. 10.1603/0022-2585(2008)45[563:caodaa]2.0.co;2 [DOI] [PubMed] [Google Scholar]
  36. Rothman, S. E. , Jones, J. A. , LaDeau, S. L. , & Leisnham, P. T. (2021). Higher West Nile virus infection in Aedes albopictus (Diptera: Culicidae) and Culex (Diptera: Culicidae) mosquitoes from lower income neighborhoods in Urban Baltimore, MD. Journal of Medical Entomology, 58(3), 1424–1428. 10.1093/jme/tjaa262 [DOI] [PubMed] [Google Scholar]
  37. Ruiz, M. O. , Tedesco, C. , McTighe, T. J. , Austin, C. , & Kitron, U. (2004). Environmental and social determinants of human risk during a West Nile virus outbreak in the greater Chicago area, 2002. International Journal of Health Geographics, 3(1), 8. 10.1186/1476-072x-3-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Ryan, S. J. , Carlson, C. J. , Tesla, B. , Bonds, M. H. , Ngonghala, C. N. , Mordecai, E. A. , et al. (2021). Warming temperatures could expose more than 1.3 billion new people to Zika virus risk by 2050. Global Change Biology, 27(1), 84–93. 10.1111/gcb.15384 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Ryan, S. J. , McNally, A. , Johnson, L. R. , Mordecai, E. A. , Ben‐Horin, T. , Paaijmans, K. , & Lafferty, K. D. (2015). Mapping physiological suitability limits for malaria in Africa under climate change. Vector Borne and Zoonotic Diseases, 15(12), 718–725. 10.1089/vbz.2015.1822 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Shocket, M. S. , Verwillow, A. B. , Numazu, M. G. , Slamani, H. , Cohen, J. M. , El Moustaid, F. , et al. (2020). Transmission of West Nile and five other temperate mosquito‐borne viruses peaks at temperatures between 23°C and 26°C. Elife, 9, e58511. 10.7554/eLife.58511 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Simpson, J. E. , Hurtado, P. J. , Medlock, J. , Molaei, G. , Andreadis, T. G. , Galvani, A. P. , & Diuk‐Wasser, M. A. (2012). Vector host‐feeding preferences drive transmission of multi‐host pathogens: West Nile virus as a model system. Proceedings of the Royal Society of London B Biological Sciences, 279(1730), 925–933. 10.1098/rspb.2011.1282 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Thibeault, J. M. , & Seth, A. (2014). Changing climate extremes in the northeast United States: Observations and projections from CMIP5. Climatic Change, 127(2), 273–287. 10.1007/s10584-014-1257-2 [DOI] [Google Scholar]
  43. Turell, M. J. , Dohm, D. J. , Sardelis, M. R. , O’guinn, M. L. , Andreadis, T. G. , & Blow, J. A. (2005). An update on the potential of North American mosquitoes (Diptera: Culicidae) to transmit West Nile virus. Journal of Medical Entomology, 42(1), 57–62. 10.1093/jmedent/42.1.57 [DOI] [PubMed] [Google Scholar]
  44. US Census Bureau . (2017). Intercensal estimates of the resident population for counties and states: April 1, 2000 to July 1, 2010. US Census Bureau. Retrieved from https://www.census.gov/data/datasets/time-series/demo/popest/intercensal-2000-2010-counties.html
  45. US Census Bureau . (2019). Population, population change, and estimated components of population change: April 1, 2010 to July 1, 2019 (CO‐EST2019‐alldata). US Census Bureau. Retrieved from https://www.census.gov/data/tables/time-series/demo/popest/2010s-counties-total.html
  46. US Census Bureau . (2021). TIGER/line shapefiles. United States Census Bureau. Retrieved from https://www.census.gov/geo/maps-data/data/tiger-line.html
  47. USGCRP . (2018). Chapter 18: Northeast. In Reidmiller D. R., Avery C. W., Easterling D. R., Kunkel K. E., Lewis K. L. M., et al. (Eds.), Impacts, risks, and adaptation in the United States: Fourth national climate assessment, volume II. U.S. Global Change Research Program. Retrieved from https://nca2018.globalchange.gov/chapter/18/ [Google Scholar]
  48. Wimberly, M. C. , & Davis, J. K. (2019). GRIDMET_downloader.js (Version 1.1). University of Oklahoma. Retrieved from https://github.com/ecograph/arbomap

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Keyel, A. C. (2022a). Supplemental data for “Patterns of West Nile virus in the northeastern United States using negative binomial and mechanistic trait‐based models”. [Dataset]. Zenodo. 10.5281/zenodo.7311872 [DOI] [PMC free article] [PubMed]
  2. Keyel, A. C. (2022b). Code for “Patterns of West Nile virus in the northeastern United States using negative binomial and mechanistic trait‐based models”. Zenodo. 10.5281/zenodo.7311878 [DOI] [PMC free article] [PubMed]
  3. R Core Team . (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/
  4. US Census Bureau . (2017). Intercensal estimates of the resident population for counties and states: April 1, 2000 to July 1, 2010. US Census Bureau. Retrieved from https://www.census.gov/data/datasets/time-series/demo/popest/intercensal-2000-2010-counties.html
  5. US Census Bureau . (2019). Population, population change, and estimated components of population change: April 1, 2010 to July 1, 2019 (CO‐EST2019‐alldata). US Census Bureau. Retrieved from https://www.census.gov/data/tables/time-series/demo/popest/2010s-counties-total.html
  6. US Census Bureau . (2021). TIGER/line shapefiles. United States Census Bureau. Retrieved from https://www.census.gov/geo/maps-data/data/tiger-line.html
  7. Wimberly, M. C. , & Davis, J. K. (2019). GRIDMET_downloader.js (Version 1.1). University of Oklahoma. Retrieved from https://github.com/ecograph/arbomap

Supplementary Materials

Figure S1

Click here for additional data file. (203.7KB, docx)

Data Availability Statement

Neuroinvasive case data are available from the Centers for Disease Control Division of Vector‐borne Diseases upon request (dvbid2@cdc.gov), subject to a data use agreement. Maps were created using ArcGIS 10.6.1 (Redlands, CA) with 2017 US Census TIGER/Shapefiles as the county base layer (US Census Bureau, 2021). Population data were derived from US Census data (US Census Bureau, 2017, 2019). Code used to run the analysis is available from Zenodo (Keyel, 2022b). Data tables with expanded results are also available from Zenodo (Keyel, 2022a).

Articles from GeoHealth are provided here courtesy of Wiley

RESOURCES