Remove redundant URLs

inst/extdata/citations/cfr.bib

@@ -3,7 +3,6 @@ @article{nilles_sars-cov-2_2022
 	title = {{SARS}-{CoV}-2 seroprevalence, cumulative infections, and immunity to symptomatic infection – {A} multistage national household survey and modelling study, {Dominican} {Republic}, {June}–{October} 2021},
 	volume = {16},
 	issn = {2667-193X},
-	url = {https://doi.org/10.1016/j.lana.2022.100390},
 	doi = {10.1016/j.lana.2022.100390},
 	abstract = {BackgroundPopulation-level SARS-CoV-2 immunological protection is poorly understood but can guide vaccination and non-pharmaceutical intervention priorities. Our objective was to characterise cumulative infections and immunological protection in the Dominican Republic.},
 	urldate = {2023-12-06},

inst/extdata/citations/epiverse.bib

@@ -14,7 +14,6 @@ @article{10.2807/1560-7917.ES.2022.27.15.2200302
    number = "15",
    eid = 2200302,
    pages = "",
-   url = "https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2022.27.15.2200302",
    doi = "10.2807/1560-7917.ES.2022.27.15.2200302"
 }
 

inst/extdata/citations/serofoi.bib

@@ -7,7 +7,6 @@ @article{10.1371/journal.pcbi.1011384
     year = {2023},
     month = {08},
     volume = {19},
-    url = {https://doi.org/10.1371/journal.pcbi.1011384},
     pages = {1-25},
     abstract = {serosim is an open-source R package designed to aid inference from serological studies, by simulating data arising from user-specified vaccine and antibody kinetics processes using a random effects model. Serological data are used to assess population immunity by directly measuring individuals’ antibody titers. They uncover locations and/or populations which are susceptible and provide evidence of past infection or vaccination to help inform public health measures and surveillance. Both serological data and new analytical techniques used to interpret them are increasingly widespread. This creates a need for tools to simulate serological studies and the processes underlying observed titer values, as this will enable researchers to identify best practices for serological study design, and provide a standardized framework to evaluate the performance of different inference methods. serosim allows users to specify and adjust model inputs representing underlying processes responsible for generating the observed titer values like time-varying patterns of infection and vaccination, population demography, immunity and antibody kinetics, and serological sampling design in order to best represent the population and disease system(s) of interest. This package will be useful for planning sampling design of future serological studies, understanding determinants of observed serological data, and validating the accuracy and power of new statistical methods.},
     number = {8},