Distribution of Vector Abundance and Infection Rates in Relation to Human Vector-Borne Disease Cases in Nebraska

This study analyzes retrospective surveillance data from 2012 to 2024 to characterize the distribution of mosquito and tick vectors and their pathogen infection rates in Nebraska, finding that while mosquito-borne diseases remain the primary concern with significant geographic variation, data limitations currently hinder definitive conclusions about the direct relationship between vector factors and human disease incidence.

The Gist

Imagine Nebraska as a giant, open-air garden. In this garden, there are invisible "gardeners" (mosquitoes and ticks) that can accidentally spread "weeds" (diseases) to the people living there. This study is like a gardener's logbook, where researchers tried to figure out: How many gardeners are out there? Are they carrying weeds? And does that mean the people in the garden are going to get sick?

Here is the story of what they found, broken down into simple parts:

1. The Two Types of Gardeners

The researchers looked at two main groups of pests:

• The Mosquitoes (The Airborne Sprinklers): Specifically, they watched two types: Culex tarsalis (the big, rural mosquito) and Culex pipiens (the city mosquito).
• The Ticks (The Hiking Boots): They looked at three types: the Lone Star tick, the American dog tick, and the deer tick.

2. The Mosquito Mystery: The "West Nile" Storm

The biggest problem in Nebraska's garden is West Nile Virus (WNV). Think of WNV as a storm that hits every summer.

• Where the storm hits hardest: The researchers found that the "storm" is strongest in Western Nebraska. This is where the Culex tarsalis mosquitoes are most abundant, and they are carrying the virus.
• The Connection: Generally, where there are more mosquitoes carrying the virus, there are more sick people. It's like a rain gauge: more rain (virus in mosquitoes) usually means more flooding (sick people).
• The Confusing Twist: When they looked at the data from just the last few years (2021–2024), they found a weird glitch. In some places, the city mosquitoes (Culex pipiens) seemed to have more virus, but fewer people got sick.
  • The Analogy: Imagine a delivery truck (the mosquito) that is full of packages (virus), but it's stuck in a traffic jam in the city and never actually drops the packages off at people's houses. The researchers think this might be because the traps used to catch mosquitoes aren't catching the right ones, or the city mosquitoes don't bite humans as often as the country mosquitoes do.

3. The Tick Situation: A Slow-Moving Invasion

Ticks are slower than mosquitoes, but they are spreading their territory like a slow-moving vine.

• The Map:
  • American Dog Ticks are everywhere, like weeds in a lawn.
  • Lone Star Ticks are mostly in the southeast, like a specific patch of ivy.
  • Deer Ticks (which carry Lyme disease) are only found in two small spots in the northeast. They are the "newcomers" that are just starting to move in.
• The Infection: The ticks were found carrying germs that cause diseases like Tularemia, Ehrlichiosis, and Lyme disease.
• The Disconnect: The researchers tried to see if "more infected ticks" meant "more sick people." It was hard to tell.
  • The Analogy: It's like trying to predict a traffic accident by counting how many cars have broken brakes. Just because a car has bad brakes (infected tick) doesn't mean it will crash (sick person) if the driver is careful or if the car never hits a pedestrian. Also, many people don't report getting sick, so the data is like a foggy window.

4. Why Was It Hard to Get a Clear Answer?

The researchers faced a few "foggy windows" in their data:

• Missing Pieces: They only had mosquito traps in 40 out of 93 counties. It's like trying to predict the weather for the whole state by only looking out the window in one town.
• The "Six-Person" Rule: In Nebraska, if a health district has fewer than six sick people, they hide the number to protect privacy. This made it hard to do the math on the smaller towns.
• The Trap Bias: The traps used to catch mosquitoes are great at catching rural mosquitoes but miss the city ones. It's like trying to count all the fish in a lake using only a net designed for big fish, while the small fish swim right under it.

5. The Big Takeaway

Even with the foggy windows, the main message is clear: Nebraska is a high-risk zone for mosquito-borne diseases, especially West Nile Virus in the west.

• Mosquitoes: The current system works okay for spotting big outbreaks, but we need to expand the "rain gauges" (traps) to more counties to get a better picture.
• Ticks: The tick situation is changing. New types of ticks are moving in, and they are carrying dangerous germs. We need to keep watching them closely, like watching a slow-moving vine that could eventually cover the whole garden.

In short: The garden is getting a bit wilder. The mosquitoes are the biggest immediate threat, but the ticks are quietly expanding their territory. To keep the people in the garden safe, the researchers say we need to keep our eyes wide open, set up more traps, and connect the dots between what the bugs are doing and what the people are feeling.

Technical Summary

1. Problem Statement

Vector-borne diseases (VBDs) represent a growing public health burden in the United States, particularly in the Great Plains region. Nebraska faces a dual threat:

• Mosquito-borne diseases (MBDs): West Nile Virus (WNV) is endemic and causes significant neuroinvasive disease, yet the quantitative relationship between vector abundance/infection rates and human case incidence remains unclear.
• Tick-borne diseases (TBDs): While historically less common than in the Northeast, TBDs (e.g., Lyme disease, ehrlichiosis, tularemia) are emerging in Nebraska. The state has seen the establishment of Ixodes scapularis (blacklegged tick) and rising cases of other pathogens, but the correlation between tick infection rates and human disease reporting is poorly understood.

The study aims to assess retrospective trends in vector abundance and pathogen infection rates and determine if these entomological metrics serve as actionable predictors for human VBD risk across Nebraska.

2. Methodology

The study utilized a retrospective analysis of entomological surveillance data and human epidemiological data from 2012 to 2024.

• Data Sources:

  • Mosquito Data: 2012–2024 data from CDC ArboNET (human cases) and Nebraska Public Health Laboratory (NPHL) mosquito trapping (light traps) in 40 counties.
  • Tick Data: 2021–2024 data from Nebraska Department of Health and Human Services (DHHS) involving dragging/flagging in 56 counties.
  • Human Cases: Aggregated county-level data for WNV and health department-level data for TBDs (anaplasmosis, ehrlichiosis, Lyme, rickettsiosis, tularemia).

• Analytical Approach:

  • Mosquito Analysis: Calculated average annual abundance (per trap night) and Pooled Infection Rates (PIR) per 1,000 mosquitoes for Culex tarsalis and Culex pipiens. Spatial autocorrelation (Global Moran's I) was used to assess WNV clustering.
  • Statistical Modeling:
    • Long-term (2012–2024): Negative binomial regression to test associations between vector metrics and WNV case counts.
    • Contemporary (2021–2024): A subset of 9 counties with consistent data was analyzed using negative binomial models (for case counts) and gamma models (for cumulative incidence) to account for overdispersion and non-normality.
  • Tick Analysis: Due to data suppression rules (counts <6), TBD analysis was conducted at the health department level. Logistic regression and Wilcoxon rank-sum tests compared tick PIRs between regions with and without reported human cases.

3. Key Results

Mosquito-Borne Diseases (WNV)

• Spatial Distribution: Culex tarsalis abundance and WNV PIRs were highest in western Nebraska, correlating with the highest human WNV cumulative incidence. Culex pipiens was more prevalent in the east.
• Clustering: Human WNV cases showed significant spatial clustering in western Nebraska (Global Moran's I z-score = 3.66, p < 0.01).
• Statistical Associations:
  • In the long-term (2012–2024) model, neither vector abundance nor PIRs for Cx. tarsalis or Cx. pipiens were significant predictors of human case counts.
  • In the contemporary subset (2021–2024), Cx. pipiens PIR showed a statistically significant but negative association with human case counts (confidence interval < 1.0). This counterintuitive result is likely attributed to sampling bias (CDC light traps are less effective for Cx. pipiens than gravid traps) or complex ecological factors, rather than a true biological inverse relationship.
  • Culex tarsalis metrics remained non-significant predictors in the contemporary model.

Tick-Borne Diseases

• Vector Distribution:
  • Dermacentor variabilis (American dog tick) was ubiquitous across the state.
  • Amblyomma americanum (Lone Star tick) was concentrated in the southeast.
  • Ixodes scapularis (Blacklegged tick) was found in two foci in the northeast, with the highest abundance in the far northeast.
• Pathogen Detection:
  • A. americanum tested positive for Ehrlichia chaffeensis and E. ewingii.
  • D. variabilis tested positive for E. ewingii, Francisella tularensis, and Rickettsia rickettsii.
  • I. scapularis tested positive for Anaplasma phagocytophilum, Borrelia miyamotoi, and Borrelia burgdorferi (Lyme).
• Human Case Correlation:
  • Human TBD cases (anaplasmosis, ehrlichiosis, rickettsiosis, tularemia) were sporadic but present in multiple regions.
  • Statistical Findings: Logistic regression found no significant relationship between tick PIRs and human case presence. A Wilcoxon rank-sum test showed a non-significant trend where higher Ehrlichia infection rates in A. americanum correlated with human cases. Notably, A. phagocytophilum detection in I. scapularis was significantly associated with health departments that reported no human anaplasmosis cases (p < 0.05), suggesting a disconnect between vector infection and reported human disease, likely due to underreporting or low human exposure.

4. Key Contributions

• Statewide Integration: This is one of the first studies to comprehensively integrate statewide entomological surveillance (both mosquito and tick) with human epidemiological data in Nebraska.
• Spatial Risk Mapping: The study provides high-resolution maps confirming that WNV risk is heavily concentrated in western Nebraska, aligning with Cx. tarsalis distribution.
• Emerging Threats: It documents the establishment of I. scapularis and the presence of multiple tick-borne pathogens (Borrelia, Anaplasma, Ehrlichia) in Nebraska, highlighting the shifting landscape of TBDs.
• Surveillance Gaps: The analysis highlights critical limitations in current surveillance, including the mismatch between trap types (light vs. gravid) and vector species, and the suppression of low-count human data which hinders granular analysis.

5. Significance and Conclusions

• Primary Driver: WNV remains the dominant VBD threat in Nebraska. While entomological surveillance is valuable, the study suggests that current metrics (abundance/PIR) do not linearly predict human case counts at the county level, likely due to reporting biases and ecological complexity.
• Tick Surveillance Urgency: The detection of I. scapularis and associated pathogens confirms that Nebraska is transitioning into a region with established Lyme disease risk, alongside persistent risks from other TBDs.
• Recommendations:
  • Expand Surveillance: Increase the number of counties conducting mosquito and tick surveillance (currently only ~22 counties for mosquitoes).
  • Methodological Adjustments: Utilize species-specific trapping methods (e.g., gravid traps for Cx. pipiens) to improve infection rate accuracy.
  • Data Integration: Strengthen the linkage between vector data and human case reporting to improve early warning systems.
  • Public Health Action: Continued investment in integrated surveillance is essential to detect emerging threats (e.g., Alpha-gal syndrome, Theileriosis) and guide targeted interventions.

The study concludes that while Nebraska has robust surveillance systems, the direct translation of vector infection rates to human risk requires more refined data collection and modeling to address underreporting and sampling biases.