Figures and data in Tracking West Nile virus dynamics using viral loads from trapped mosquitoes

Figures
Tables
Additional files

31 figures, 3 tables and 1 additional file

Figures

Figure 1

Download asset Open asset

Observed data.

(A): Calculated pooled Ct value vs. disease week from 2022 to 2024. Ct values are presented from pools of mosquitoes separated by species, either *C. pipiens/restuans/salinarius* or *C. tarsalis*. (B): RT-qPCR test results vs. disease week from 2022 to 2024.

Figure 2

Download asset Open asset

Overall structure of the agent-based model (parameters were derived from the literature; see Appendix 1, Sections 2, 3, and 4).

(A) Transmission cycle of WNV between birds (hosts) and mosquitoes (vectors). Female mosquitoes lay eggs, which hatch after an average of 8 days, before developing into adult female mosquitoes that contribute to the transmission cycle. (B) Transmission occurs with some probability when an infected female mosquito bites a susceptible bird or a susceptible female mosquito bites an infected bird. (C) Within-host viral kinetics model. Infected birds follow an acute viral load trajectory with rising and clearance phases, starting at the RT-qPCR limit of detection. Female mosquitoes may follow one of the two different viral kinetics models: the mosquito’s initial viral load is proportional to the infector bird’s viral load at the time of bite and either leads to a non-replicating viral trajectory (static viral load) (i) or a replication-competent infection (dynamic viral load) (ii). Note that the Ct value scale is inverted, with lower Ct values depicting higher on the scale. (D) Overall population-level dynamics of the agent-based model. If bitten by an infected mosquito, susceptible birds ( $S$ ) can become infectious ( $I$ ). Once infectious, they will eventually recover ( $R$ ), with lifelong immunity following their infectious period. Susceptible mosquitoes ( $S$ ), if they bite infectious birds, will become infectious ( $I$ ) for the rest of their lifespan. Births and deaths are possible in all these states for both birds and mosquitoes. (E) Simulation of routine mosquito trapping and pooled testing accounting for blood meal-seeking behaviour. For each pool, a combined pooled Ct value is calculated based on the mean viral load of captured WNV-positive mosquitoes, diluted by the contribution of WNV-negative mosquitoes. (F) Example population-level Ct dynamics from the simulation. Note that the Ct value scale is inverted, with lower Ct values depicting higher on the scale.

Figure 3

Download asset Open asset

Simulated population-level WNV infection dynamics in mosquitoes.

Each line represents one year of simulated data, and the bold black line highlights the simulated trajectory used for (C). (A) Weekly mosquito infection prevalence per capita. (B) Weekly mosquito infection incidence per capita. (C) Individual-level Ct values of the female mosquito population from 1 year of a single simulation. Each point represents a Ct value from a single randomly selected mosquito, representing perfect observations of the entire population at all times. (D) Calculated pooled Ct values from the mosquito population based on the individual-level Ct values in (C).

Figure 4

Download asset Open asset

Distributions of the pooled Ct values within the limit of detection.

(A) Simulated Ct values by assuming all mosquitoes undergo dynamic infection from Ct = 40. (B) Simulated Ct values assuming mosquitoes inherit 100% of viral load from birds, and 50% of mosquitoes undergo a static viral kinetics trajectory. The bottom left and right panels. (C) and (D) The density of pooled Ct values from Nebraska and Colorado states over 3 years, respectively.

Figure 5

Download asset Open asset

Prevalence estimation using pooled Ct values and binarised data.

(A) Weekly pooled Ct values from the captured mosquitoes (using the sampling procedure used in the agent-based model). Each dot represents the Ct value calculated per pool. (B) Binarised data based on the pooled Ct values in (A). Binarisation was based on whether or not the pooled Ct values were within the limit of detection. (C) Estimated proportion of productive infection using the pooled Ct values. The pink ribbon shows the 95% confidence interval. (E) The estimated weekly WNV prevalence among mosquitoes using the binarised data and the *PooledInfRate* R package (green line). The green ribbon shows the 95% confidence interval. These values are compared with the true prevalence from the simulation (blue dashed line). (F) Estimated productive infection prevalence using pooled Ct values. The purple ribbon shows the 95% confidence interval.

Figure 6

Download asset Open asset

Comparison of prevalence estimates based on the pooled Ct values and the *PooledInfRate* R package with varying pool sizes.

The beige colour dots present maximum likelihood prevalence estimates calculated from pooled Ct values from a different simulation. The estimated prevalence calculated using the binarised data is in dark red. Each panel presents the estimates based on 100 pools containing up to 50 mosquitoes (pool size). The black dashed line represents perfect estimates. Labels along with the red dotted lines illustrate the maximum prevalence level at which the *PooledInfRate* R package returns accurate estimates.

Figure 7

Download asset Open asset

Overall prevalence estimation per year for Nebraska and Colorado from 2022 to 2024 using pooled Ct values (green) and binarised data (red), and the estimated productive infection prevalence (beige).

The lines show mean estimates and the ribbons are 95% confidence intervals. Red dashed lines are the estimated prevalence based on binarised data.

Appendix 1—figure 1

Download asset Open asset

Human case data and mosquito surveilance data.

(A) Observed monthly human cases from 1999 to 2024 (downloaded from the CDC website CDC, 2025). (C) and (D) [Figure 1 of the manuscript]: Observed data: Calculated pooled Ct value vs. disease week from 2022 to 2024. Pooled Ct values presented here are calculated by using species *Culex pipiens/restuans/salinarius* and *Culex tarsalis*.

Appendix 1—figure 2

Download asset Open asset

Observed Ct values distributed by number of mosquitoes contained in the tested pool.

Pearson correlation coefficient = 0.04, p-value = 0.31.

Appendix 1—figure 3

Download asset Open asset

Observed Ct values from 143 samples tested in two laboratories.

Ct values generated from the UNMC laboratory were consistently lower than those generated from NPHL, showing reproducibility of Ct value data from the same mosquito pool.

Appendix 1—figure 4

Download asset Open asset

Ct values by species in Nebraska from 2022 to 2024 and Colorado from 2022 to 2023.

Here, pipiens in Nebraska include *C. pipiens/restuans/salinarius* species.

Appendix 1—figure 5

Download asset Open asset

Ct values obtained by counties in Nebraska.

Appendix 1—figure 6

Download asset Open asset

Ct values obtained by counties in Colorado.

Appendix 1—figure 7

Download asset Open asset

Time-dependent parameters for the mosquitoes for 1 year.

(A) Birth rate, $b_{M t}$ . (B) Transmission probability from bird to mosquito, $p_{M I t}$ . (C) Biting rate, $r_{t}$ . (D) Daily death rate, $d_{M t}$ .

Appendix 1—figure 8

Download asset Open asset

Probability of overwintering (top panel) and that of the mosquitoes becoming active (bottom panel) during 1 year period.

Appendix 1—figure 9

Download asset Open asset

Ct dynamics models used for birds (top panel) and the mosquitoes (bottom panel) in our agent-based model.

Top panel: $m_{i}$ (where $i = 1, 2$ ) represents the slope parameters for the birds. $C_{[B i]}$ ( $i = 1, 2$ ) represents a random Ct value within a bird since infection. Bottom panel: (a) Ct dynamics model when the mosquitoes undergo dynamic infection from $C t_{[L O D]}$ , the limit of detection (which is 40 in our study). $m$ represents the slope parameter of a mosquito in the Ct model. $y_{t}$ represents a random Ct value of a mosquito since infection. (b) Mosquitoes inherit a certain percentage of viral load from birds. (i) shows the static viral load, and (ii) illustrates the dynamic viral load models. $C t_{[B]}$ represents the Ct value inherited from the bird at the time of a successful infection of a mosquito. For both bird and mosquito Ct dynamics models, $C t_{[L O D]}$ represents the limit of detection (Ct = 40), $t_{0}$ is the time of infection, and $C t_{[M I N]}$ is the minimum Ct value that reaches the peak viral load.

Appendix 1—figure 10

Download asset Open asset

Relationship between log10(viral load) and Ct values.

Appendix 1—figure 11

Download asset Open asset

Appendix 1—figure 12

Download asset Open asset

Appendix 1—figure 13

Download asset Open asset

Simulated population-level WNV infection dynamics in mosquitoes.

Each line represents 1 year of simulated data, and the bold black line highlights the simulated trajectory used for (C) and Figure 4 in the manuscript. (A) Weekly bird infection prevalence per capita. (B) Weekly bird infection incidence per capita. (C) Individual-level Ct values of the bird population from 1 year of a single simulation. Each point represents a Ct value from a single randomly selected bird, representing perfect observations of the entire population at all times.

Appendix 1—figure 14

Download asset Open asset

Top panel: Density distributions of the Ct values within the level of detection with change of viral inheritance probability vs. model change probability.

Each density simulated per year is overlaid. Model change for mosquitoes was done as a Bernoulli trial with the probability specified in each sup-panel in the right corner. A successful Bernoulli trial indicated model (b) i: that is, the mosquito viral load will remain constant after successful viral inheritance. Bottom panel: Density distributions of the Ct values within the level of detection for the Colorado and Nebraska datasets. All the densities for the three years are overlaid.

Appendix 1—figure 15

Download asset Open asset

Left: Density distributions of the Ct values within the level of detection with change of viral inheritance probability = 1 vs. model change probability = 0.5 with assumed decay rates 0, 0.001, 0.01, 0.1, and 0.5.

Each density simulated per year is overlaid. Right: Ct dynamics with assumed decay rates 0, 0.001, 0.01, 0.1, and 0.5.

Appendix 1—figure 16

Download asset Open asset

Density distributions of Ct values of the mosquitoes (less than the limit of detection) from simulated data assuming mosquitoes inherit 100% of viral load from birds, and 50% of mosquitoes undergo a static viral kinetics trajectory.

Each panel illustrates the distributions of the Ct values of the mosquitoes that underwent a static (in pink) or dynamic (in blue) viral kinetics trajectory in each week.

Appendix 1—figure 17

Download asset Open asset

Distributions of estimated $P r (y_{j} | k_{j}, n_{j})$ through KDE approximations.

Each line represents the number of positive samples ( $k_{j}$ ). Each panel represents the pool size, and $n_{j}$ depends on the pool size.

Appendix 1—figure 18

Download asset Open asset

Distributions of estimated $P r (y_{j} | k_{j}, n_{j})$ through KDE approximations when the pool size is 50.

Each panel represents the number of positive samples ( $k_{j}$ ). $n_{j}$ is assumed to be 50.

Appendix 1—figure 19

Download asset Open asset

Estimated prevalence using 8 simulations.

Beige lines and ribbons illustrate the estimated prevalence and the corresponding 95% confidence intervals using pooled Ct values. The green lines and ribbons illustrate the estimated prevalence and the corresponding 95% confidence intervals using the binarised data.

Appendix 1—figure 20

Download asset Open asset

Using Pooled Ct values, the difference between actual prevalence and the estimated prevalence under different prevalence values, pool sizes, and number of pools.

Each combination was repeated 100 times. All the calculations are done using the MLE estimates for prevalence. Black lines represent the mean of the estimates. The ribbons are 95% quantile intervals based on the MLE estimates. If the MLE estimate is the same across the 100 repeated calculations (see top corner panel when the prevalence = 0.001, the quantiles were not calculated.).

Appendix 1—figure 21

Download asset Open asset

The percentages of 95% confidence intervals that include the true prevalence.

95% confidence intervals were calculated using the same combinations of number of pools, prevalence values, and pool sizes as in Appendix 1—figure 20. Each combination was repeated 100 times, and under each of these measures, each 95% confidence interval was checked to see if the interval covered the true prevalence.

Appendix 1—figure 22

Download asset Open asset

Top panel: Productive infection estimation error.

Box plots of the difference between actual prevalence and the estimated productive infection prevalence using Pooled Ct values under different prevalence values, number of pools and pool size of 50. Each combination was repeated 100 times. All the calculations are done using the MLE estimates for prevalence. Bottom panel: Confidence interval coverage. The percentages of 95% confidence intervals that include the true productive infection prevalence. 95% confidence intervals were calculated using the same combinations of number of pools, prevalence values, and pool sizes as in top panel. Each combination was repeated 100 times, and under each of these measures, each 95% confidence interval was checked to see if the interval covered the true prevalence.

Appendix 1—figure 23

Download asset Open asset

The percentages of 95% confidence intervals that include the true productive infection prevalence.

95% confidence intervals were calculated using the same combinations as in Appendix 1—figure 22. Each combination was repeated 100 times, and under each of these measures, each 95% confidence interval was checked to see if the interval covered the true prevalence. If the interval included the true productive infection prevalence, it is denoted in blue, otherwise in red. Note: Some confidence intervals are narrow (e.g.: see coverage under 100 pools, productive infection proportion 0.6 and production infection prevalence 0.001). Therefore, the confidence intervals may not be visible.

Appendix 1—figure 24

Download asset Open asset

Estimated productive infection proportion from Nebraska and Colorado data based on the results in Figure 7 in the manuscript.

Tables

Appendix 1—table 1

Population-level parameters of the agent-based model.

Parameter	Description	Values	Reference
T	Simulation time	26 years (excluding a burn-in period of one year)	NA
$t$	Current time of the simulation (in days)	NA	NA
$N_{H}$	Number of hosts(birds) at t = 0 (initial bird population size)	1000	NA
$N_{M}$	Number of female mosquitoes at t = 0 (initial mosquito population size)	10,000	Female mosquito to bird ratio 1:10 (Vogels et al., 2017; Balenghien et al., 2006)
$I_{M 0}$	Infected female mosquitoes att = 0	100	NA
$P_{H I}$	Transmission probability from mosquito to bird per bite	0.88	Turell et al., 2001
$b_{M t}$	Average number of eggs laid by mosquitoes who will eventually become adult female mosquitoes per day.	See details in Section 2.1
$a_{E A}$	The number of days on average that takes for an egg to become an adult mosquito	8	CDC (CDC, 2024c)
$b_{H}$	Bird birth rate per day.	$0.023 \times N_{H}$ per day	0.023: (Ferraguti et al., 2023)
$d_{H}$	Bird death rate	0.0015 per day	Ferraguti et al., 2023
$d_{M t}$	Daily death rate of mosquitoes	See details in Section 2.4
$p_{M I t}$	Time-dependent transmission probability from bird to mosquitoes per bite	See details in Section 2.2
$r_{t}$	Daily biting rate	See details in Section 2.3
$O_{t}$	Overwintering period (time-dependent)	See details in Section 2.5
$p_{o t}$	Probability overwintering (time-dependent)	See details in Section 2.5
$A_{t}$	Mosquito active period (time-dependent)	See details in Section 2.6
$p_{a t}$	Probability of mosquitoes becoming active after overwintering (time-dependent)	See details in Section 2.6

Appendix 1—table 2

Summary statistics for the Ct values from Nebraska from 2022 to 2024.

The 95% confidence intervals from both estimation methods partially overlap in all weeks across all years.

Year	Disease week	Number of pools	Mean Ct (including negative pools)	Minimum Ct	Number of positives	Mean Ct (with positive pools)	Average pool size	Estimated prevalence: binary data (95% CI)	Estimated prevalence: pooled Ct values (95% CI)	Estimated productive infection prevalence: pooled Ct values (95% CI)
2022	22	17	40.00	40.00	0		2	0 (0, 0.092)	0 (0, 0.05)	0 (0, 1)
2022	23	33	40.00	40.00	0		6	0 (0, 0.018)	0 (0, 0.01)	0 (0, 1)
2022	24	25	40.00	40.00	0		3	0 (0, 0.045)	0 (0, 0.025)	0 (0, 1)
2022	25	44	40.00	40.00	0		6	0 (0, 0.014)	0 (0, 0.01)	0 (0, 1)
2022	26	36	40.00	40.00	0		4	0 (0, 0.023)	0 (0, 0.01)	0 (0, 1)
2022	27	36	40.00	40.00	0		7	0 (0, 0.014)	0 (0, 0.01)	0 (0, 1)
2022	28	41	39.70	27.78	1	27.78	6	0.004 (0, 0.019)	0.004 (0, 0.019)	0.004 (0, 0.021)
2022	29	45	40.00	40.00	0		11	0 (0, 0.008)	0 (0, 0.005)	0 (0, 1)
2022	30	28	40.00	40.00	0		6	0 (0, 0.022)	0 (0, 0.01)	0 (0, 1)
2022	31	32	39.16	21.62	2	26.52	12	0.005 (0.001, 0.016)	0.006 (0.001, 0.018)	0.005 (0.001, 0.02)
2022	32	70	39.69	27.26	2	29.04	22	0.001 (0, 0.004)	0.001 (0, 0.005)	0.001 (0, 0.004)
2022	33	60	38.50	23.27	8	28.74	21	0.007 (0.003, 0.014)	0.008 (0.004, 0.015)	0.005 (0.002, 0.012)
2022	34	79	38.33	20.84	10	26.79	18	0.008 (0.004, 0.014)	0.009 (0.004, 0.015)	0.006 (0.003, 0.012)
2022	35	107	37.21	22.01	29	29.72	29	0.012 (0.008, 0.016)	0.012 (0.008, 0.017)	0.007 (0.004, 0.01)
2022	36	92	37.05	21.20	19	25.74	20	0.013 (0.008, 0.02)	0.015 (0.009, 0.022)	0.012 (0.007, 0.019)
2022	37	74	37.96	19.40	11	26.26	18	0.01 (0.005, 0.017)	0.011 (0.006, 0.019)	0.007 (0.004, 0.014)
2022	38	66	38.88	19.13	5	25.21	13	0.006 (0.002, 0.013)	0.007 (0.002, 0.015)	0.005 (0.002, 0.013)
2022	39	41	40.00	40.00	0		5	0 (0, 0.018)	0 (0, 0.01)	0 (0, 1)
2023	22	53	40.00	40.00	0		6	0 (0, 0.013)	0 (0, 0.005)	0 (0, 1)
2023	23	35	40.00	40.00	0		6	0 (0, 0.017)	0 (0, 0.01)	0 (0, 1)
2023	24	53	40.00	40.00	0		6	0 (0, 0.011)	0 (0, 0.005)	0 (0, 1)
2023	25	58	39.40	20.00	3	28.37	13	0.004 (0.001, 0.011)	0.005 (0.001, 0.012)	0.003 (0.001, 0.011)
2023	26	105	39.60	25.20	4	29.48	27	0.001 (0, 0.003)	0.002 (0, 0.004)	0.001 (0, 0.003)
2023	27	44	39.71	27.10	1	27.10	15	0.002 (0, 0.007)	0.002 (0, 0.008)	0.002 (0, 0.008)
2023	28	92	38.63	22.00	10	27.39	22	0.006 (0.003, 0.01)	0.006 (0.003, 0.011)	0.005 (0.002, 0.009)
2023	29	159	37.42	20.00	42	30.22	36	0.009 (0.006, 0.012)	0.009 (0.007, 0.012)	0.004 (0.003, 0.006)
2023	30	135	38.05	19.50	32	31.77	32	0.009 (0.006, 0.013)	0.009 (0.007, 0.013)	0.003 (0.002, 0.006)
2023	31	51	39.01	21.90	4	27.38	15	0.006 (0.002, 0.014)	0.007 (0.002, 0.015)	0.004 (0.001, 0.014)
2023	32	128	37.65	17.80	30	29.97	26	0.011 (0.008, 0.016)	0.012 (0.008, 0.016)	0.005 (0.003, 0.008)
2023	33	145	37.28	19.00	41	30.36	39	0.009 (0.006, 0.012)	0.009 (0.007, 0.013)	0.004 (0.003, 0.006)
2023	34	129	38.14	20.70	17	25.86	32	0.005 (0.003, 0.007)	0.005 (0.003, 0.008)	0.004 (0, 0.007)
2023	35	85	38.28	16.80	9	23.72	27	0.004 (0.002, 0.007)	0.005 (0.002, 0.008)	0.003 (0.002, 0.007)
2023	36	60	38.16	19.84	7	24.24	9	0.013 (0.006, 0.026)	0.014 (0.006, 0.028)	0.013 (0, 0.024)
2023	37	54	39.08	19.20	5	30.08	17	0.006 (0.002, 0.012)	0.006 (0.002, 0.013)	0.002 (0.001, 0.009)
2023	38	56	39.26	19.14	2	19.39	8	0.004 (0.001, 0.014)	0.005 (0.001, 0.015)	0.004 (0, 0.014)
2023	39	38	39.59	24.30	1	24.30	5	0.005 (0, 0.022)	0.005 (0, 0.023)	0.005 (0, 0.028)
2024	22	57	40.00	40.00	0		15	0 (0, 0.005)	0 (0, 0.005)	0 (0, 1)
2024	23	53	40.00	40.00	0		13	0 (0, 0.005)	0 (0, 0.005)	0 (0, 1)
2024	24	84	39.71	20.40	2	27.85	24	0.001 (0, 0.003)	0.001 (0, 0.003)	0 (0, 0.003)
2024	25	51	40.00	40.00	0		18	0 (0, 0.004)	0 (0, 0.005)	0 (0, 1)
2024	26	73	39.80	25.28	1	25.28	13	0.001 (0, 0.005)	0.001 (0, 0.005)	0.001 (0, 0.005)
2024	27	50	39.52	27.30	2	28.00	11	0.004 (0.001, 0.012)	0.004 (0.001, 0.013)	0.004 (0, 0.012)
2024	28	111	39.45	21.30	5	27.80	23	0.002 (0.001, 0.004)	0.002 (0.001, 0.005)	0.002 (0.001, 0.004)
2024	29	89	38.81	21.00	8	26.76	30	0.003 (0.001, 0.006)	0.004 (0.002, 0.007)	0.003 (0.001, 0.006)
2024	30	77	39.80	30.90	2	32.40	21	0.001 (0, 0.004)	0.001 (0, 0.004)	0 (0, 0.008)
2024	31	60	38.74	23.20	7	29.17	20	0.007 (0.003, 0.013)	0.007 (0.003, 0.014)	0.004 (0.002, 0.011)
2024	32	64	38.71	21.90	5	23.50	16	0.005 (0.002, 0.012)	0.006 (0.002, 0.013)	0.005 (0, 0.011)
2024	33	75	38.31	19.80	9	25.94	25	0.005 (0.002, 0.009)	0.006 (0.003, 0.01)	0.004 (0.002, 0.009)
2024	34	105	37.34	18.70	18	24.47	29	0.007 (0.004, 0.01)	0.008 (0.005, 0.012)	0.006 (0.004, 0.01)
2024	35	108	38.06	23.10	20	29.52	29	0.008 (0.005, 0.012)	0.008 (0.005, 0.012)	0.005 (0.003, 0.009)
2024	36	80	39.34	20.00	5	29.48	19	0.003 (0.001, 0.007)	0.004 (0.001, 0.008)	0.001 (0, 0.006)
2024	37	63	38.73	20.90	7	28.60	17	0.007 (0.003, 0.014)	0.008 (0.003, 0.015)	0.004 (0.002, 0.011)
2024	38	51	39.59	21.70	2	29.55	9	0.004 (0.001, 0.014)	0.005 (0.001, 0.014)	0.002 (0, 0.016)
2024	39	52	40.00	40.00	1		10	0 (0, 0.008)	0 (0, 0.005)	0 (0, 1)

Appendix 1—table 3

Summary statistics for the Ct values from Colorado from 2022 to 2023.

The 95% confidence intervals from both estimation methods partially overlap in all weeks across all years.

Year	Disease week	Number of pools	Mean Ct (including negative pools)	Minimum Ct	Number of positives	Mean Ct (with positive pools)	Average pool size	Estimated prevalence: binary data (95% CI)	Estimated prevalence: pooled Ct values (95% CI)	Estimated productive infection prevalence: pooled Ct values (95% CI)
2022	23	41	40.00	40.00	0		7	0 (0, 0.014)	0 (0, 0.005)	0 (0, 1)
2022	24	62	40.00	40.00	0		8	0 (0, 0.007)	0 (0, 0.005)	0 (0, 1)
2022	25	60	39.92	37.57	2	37.65	9	0.004 (0.001, 0.012)	0.004 (0.001, 0.013)	0 (0, 0.998)
2022	26	85	39.83	32.49	2	32.73	14	0.002 (0, 0.005)	0.002 (0, 0.005)	0 (0, 0.999)
2022	27	117	40.00	40.00	0		24	0 (0, 0.001)	0 (0, 0.005)	0 (0, 1)
2022	28	164	39.22	19.45	15	31.47	32	0.003 (0.002, 0.005)	0.003 (0.002, 0.005)	0.001 (0, 0.002)
2022	29	176	39.27	19.76	9	25.66	33	0.002 (0.001, 0.003)	0.002 (0.001, 0.003)	0.001 (0.001, 0.003)
2022	30	139	38.84	17.31	18	31.04	26	0.005 (0.003, 0.008)	0.006 (0.003, 0.009)	0.002 (0.001, 0.004)
2022	31	130	38.10	19.25	19	27.01	26	0.006 (0.004, 0.009)	0.007 (0.004, 0.01)	0.004 (0.002, 0.007)
2022	32	144	37.70	8.41	22	24.97	31	0.006 (0.004, 0.008)	0.006 (0.004, 0.009)	0.003 (0.002, 0.006)
2022	33	147	38.71	19.10	13	25.40	28	0.003 (0.002, 0.006)	0.004 (0.002, 0.006)	0.003 (0.002, 0.005)
2022	34	170	37.63	18.34	25	23.88	30	0.005 (0.003, 0.008)	0.006 (0.004, 0.009)	0.005 (0.003, 0.007)
2022	35	113	37.50	17.46	18	24.31	17	0.011 (0.007, 0.017)	0.012 (0.007, 0.018)	0.01 (0.006, 0.016)
2022	36	92	39.77	24.09	2	29.46	13	0.002 (0, 0.005)	0.002 (0, 0.005)	0.001 (0, 0.006)
2022	37	94	39.51	23.66	4	28.58	19	0.002 (0.001, 0.006)	0.003 (0.001, 0.006)	0.002 (0.001, 0.005)
2023	23	69	40.00	40.00	0		6	0 (0, 0.009)	0 (0, 0.005)	0 (0, 1)
2023	24	78	40.00	40.00	0		7	0 (0, 0.007)	0 (0, 0.005)	0 (0, 1)
2023	25	107	40.00	40.00	0		17	0 (0, 0.002)	0 (0, 0.005)	0 (0, 1)
2023	26	231	39.96	30.12	1	30.12	39	0 (0, 0)	0 (0, 0.001)	0 (0, 0)
2023	27	290	39.63	19.28	8	26.65	41	0.001 (0, 0.001)	0.001 (0, 0.001)	0 (0, 0)
2023	28	322	39.26	17.15	17	25.97	42	0.001 (0.001, 0.002)	0.001 (0.001, 0.002)	0.001 (0.001, 0.002)
2023	29	403	39.02	14.13	37	29.28	45	0.002 (0.002, 0.003)	0.002 (0.002, 0.003)	0.001 (0.001, 0.002)
2023	30	206	37.97	18.85	26	23.93	36	0.004 (0.003, 0.005)	0.004 (0.003, 0.006)	0.004 (0.003, 0.005)
2023	31	142	37.05	17.70	28	25.04	28	0.008 (0.006, 0.012)	0.009 (0.006, 0.013)	0.006 (0.004, 0.009)
2023	32	118	36.93	16.59	21	22.73	22	0.01 (0.006, 0.015)	0.011 (0.007, 0.017)	0.01 (0, 0.014)
2023	33	107	37.39	18.78	20	26.01	20	0.011 (0.007, 0.017)	0.013 (0.008, 0.019)	0.009 (0.006, 0.015)
2023	34	116	37.77	19.34	16	23.80	19	0.008 (0.005, 0.013)	0.009 (0.005, 0.014)	0.007 (0.004, 0.012)
2023	35	96	39.05	19.20	8	28.59	10	0.009 (0.004, 0.017)	0.01 (0.004, 0.018)	0.006 (0.002, 0.014)
2023	36	58	39.60	16.56	1	16.56	8	0.002 (0, 0.009)	0.002 (0, 0.009)	0.002 (0, 0.012)
2023	37	44	40.00	40.00	0		10	0 (0, 0.008)	0 (0, 0.005)	0 (0, 1)

Additional files

MDAR checklist: https://cdn.elifesciences.org/articles/111900/elife-111900-mdarchecklist1-v1.docx
Download elife-111900-mdarchecklist1-v1.docx

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Article PDF

Open citations (links to open the citations from this article in various online reference manager services)

Mendeley

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

Punya Alahakoon
Ian Marchinton
R Koch
Joseph R Fauver
James A Hay

(2026)

Tracking West Nile virus dynamics using viral loads from trapped mosquitoes

eLife 15:RP111900.

https://doi.org/10.7554/eLife.111900.3

Share this article

Cite this article

Observed data.

Overall structure of the agent-based model (parameters were derived from the literature; see Appendix 1, Sections 2, 3, and 4).

Simulated population-level WNV infection dynamics in mosquitoes.

Distributions of the pooled Ct values within the limit of detection.

Prevalence estimation using pooled Ct values and binarised data.

Comparison of prevalence estimates based on the pooled Ct values and the PooledInfRate R package with varying pool sizes.

Overall prevalence estimation per year for Nebraska and Colorado from 2022 to 2024 using pooled Ct values (green) and binarised data (red), and the estimated productive infection prevalence (beige).

Human case data and mosquito surveilance data.

Observed Ct values distributed by number of mosquitoes contained in the tested pool.

Observed Ct values from 143 samples tested in two laboratories.

Ct values by species in Nebraska from 2022 to 2024 and Colorado from 2022 to 2023.

Ct values obtained by counties in Nebraska.

Ct values obtained by counties in Colorado.

Time-dependent parameters for the mosquitoes for 1 year.

Probability of overwintering (top panel) and that of the mosquitoes becoming active (bottom panel) during 1 year period.

Ct dynamics models used for birds (top panel) and the mosquitoes (bottom panel) in our agent-based model.

Relationship between log10(viral load) and Ct values.

States of the hosts over 10 years.

States of mosquitoes over 10 years.

Simulated population-level WNV infection dynamics in mosquitoes.

Top panel: Density distributions of the Ct values within the level of detection with change of viral inheritance probability vs. model change probability.

Left: Density distributions of the Ct values within the level of detection with change of viral inheritance probability = 1 vs. model change probability = 0.5 with assumed decay rates 0, 0.001, 0.01, 0.1, and 0.5.

Density distributions of Ct values of the mosquitoes (less than the limit of detection) from simulated data assuming mosquitoes inherit 100% of viral load from birds, and 50% of mosquitoes undergo a static viral kinetics trajectory.

Distributions of estimated Pr(yj|kj,nj) through KDE approximations.

Distributions of estimated Pr(yj|kj,nj) through KDE approximations when the pool size is 50.

Estimated prevalence using 8 simulations.

Using Pooled Ct values, the difference between actual prevalence and the estimated prevalence under different prevalence values, pool sizes, and number of pools.

The percentages of 95% confidence intervals that include the true prevalence.

Top panel: Productive infection estimation error.

The percentages of 95% confidence intervals that include the true productive infection prevalence.

Estimated productive infection proportion from Nebraska and Colorado data based on the results in Figure 7 in the manuscript.

Population-level parameters of the agent-based model.

Summary statistics for the Ct values from Nebraska from 2022 to 2024.

Summary statistics for the Ct values from Colorado from 2022 to 2023.

MDAR checklist

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

Distributions of estimated $P r (y_{j} | k_{j}, n_{j})$ through KDE approximations.

Distributions of estimated $P r (y_{j} | k_{j}, n_{j})$ through KDE approximations when the pool size is 50.