Now we are ready to begin estimating seroincidence. We will use
est.incidence.by to calculate stratified seroincidence
rates.
-
+
# Using est.incidence.by (strata)
est <- est.incidence.by(
@@ -371,7 +379,7 @@ Estimate Seroincide
Now we are ready to begin estimating seroincidence. We will use
est.incidence.by to calculate stratified seroincidence
rates.
-
+
# Using est.incidence.by (strata)
est2 <- est.incidence.by(
@@ -409,7 +417,7 @@ Estimate Seroincide
#> # ℹ 5 more variables: coverage <dbl>, log.lik <dbl>, iterations <int>,
#> # antigen.isos <chr>, nlm.convergence.code <ord>
Let’s visualize our seroincidence estimates by strata.
-
+
# Plot seroincidence estimates
# Save summary(est) as a dataframe
@@ -420,25 +428,22 @@ Estimate Seroincide
est2df <- summary(est2)
-est.comb <- rbind(estdf, est2df)
+est_comb <- rbind(estdf, est2df)
# Create barplot (rescale incidence rate and CIs)
-ggplot(est.comb, aes(y = ageQ, x = incidence.rate * 1000, fill = country)) +
- geom_bar(stat = "identity", position = position_dodge2(width = 0.8, preserve = "single")) +
+ggplot(est_comb) +
+ aes(y = ageQ, x = incidence.rate * 1000, fill = country) +
+ geom_bar(stat = "identity",
+ position = position_dodge2(width = 0.8, preserve = "single")) +
geom_linerange(aes(xmin = CI.lwr * 1000, xmax = CI.upr * 1000),
- position = position_dodge2(width = 0.8, preserve = "single")
- ) +
- labs(
- title = "Enteric Fever Seroincidence by Catchment Area",
- x = "Seroincience rate per 1000 person-years",
- y = "Catchment"
- ) +
+ position = position_dodge2(width = 0.8, preserve = "single")) +
+ labs(title = "Enteric Fever Seroincidence by Catchment Area",
+ x = "Seroincience rate per 1000 person-years",
+ y = "Catchment") +
theme_bw() +
- facet_wrap(~country) +
- theme(
- axis.text.y = element_text(size = 11),
- axis.text.x = element_text(size = 11)
- ) +
+ facet_wrap(~ country) +
+ theme(axis.text.y = element_text(size = 11),
+ axis.text.x = element_text(size = 11)) +
scale_fill_manual(values = c("orange2", "#39558CFF", "red"))
Funding
-
References
+
+
References
+
+
+Aiemjoy, Kristen, Nishan Katuwal, Krista Vaidya, Sony Shrestha, Melina
+Thapa, Peter Teunis, Isaac I Bogoch, et al. Accepted Feb 2024.
+“Estimating the Seroincidence of Scrub Typhus Using Antibody
+Dynamics Following Infection.” American Journal of Tropical
+Medicine and Hygiene, Accepted Feb 2024.
+
+
+deGraaf, W. F., M. E. E. Kretzschmar, P. F. M. Teunis, and O. Diekmann.
+2014.
“A Two-Phase Within-Host Model for Immune Response and Its
+Application to Serological Profiles of Pertussis.”
+
Epidemics 9 (December): 1–7.
https://doi.org/10.1016/j.epidem.2014.08.002.
+
+
+Simonsen, J., K. Mølbak, G. Falkenhorst, K. A. Krogfelt, A. Linneberg,
+and P. F. M. Teunis. 2009.
“Estimation of Incidences of Infectious
+Diseases Based on Antibody Measurements.” Statistics in
+Medicine 28 (14): 1882–95.
https://doi.org/10.1002/sim.3592.
+
+
+Teunis, P. F. M., and J. C. H. van Eijkeren. 2020.
“Estimation of
+Seroconversion Rates for Infectious Diseases: Effects of Age and
+Noise.” Statistics in Medicine 39 (21): 2799–2814.
https://doi.org/10.1002/sim.8578.
+
+
+Teunis, P. F. M., J. C. H. van Eijkeren, W. F. de Graaf, A. Bonačić
+Marinović, and M. E. E. Kretzschmar. 2016.
“Linking the
+Seroresponse to Infection to Within-Host Heterogeneity in Antibody
+Production.” Epidemics 16 (September): 33–39.
https://doi.org/10.1016/j.epidem.2016.04.001.
+
+
+Teunis, P. F. M., JCH van Eijkeren, CW Ang, YTHP van Duynhoven, JB
+Simonsen, MA Strid, and W van Pelt. 2012.
“Biomarker Dynamics:
+Estimating Infection Rates from Serological Data.” Statistics
+in Medicine 31 (20): 2240–48.
https://doi.org/10.1002/sim.5322.
+
+