Monitoring and responding to emerging infectious diseases in a university setting: A case study using COVID-19.

Emerging infection diseases (EIDs) are an increasing threat to global public health, especially when the disease is newly emerging. Institutions of higher education (IHEs) are particularly vulnerable to EIDs because student populations frequently share high-density residences and strongly mix with local and distant populations. In fall 2020, IHEs responded to a novel EID, COVID-19. Here, we describe Quinnipiac University's response to SARS-CoV-2 and evaluate its effectiveness through empirical data and model results. Using an agent-based model to approximate disease dynamics in the student body, the University established a policy of dedensification, universal masking, surveillance testing via a targeted sampling design, and app-based symptom monitoring. After an extended period of low incidence, the infection rate grew through October, likely due to growing incidence rates in the surrounding community. A super-spreader event at the end of October caused a spike in cases in November. Student violations of the University's policies contributed to this event, but lax adherence to state health laws in the community may have also contributed. The model results further suggest that the infection rate was sensitive to the rate of imported infections and was disproportionately impacted by non-residential students, a result supported by the observed data. Collectively, this suggests that campus-community interactions play a major role in campus disease dynamics. Further model results suggest that app-based symptom monitoring may have been an important regulator of the University's incidence, likely because it quarantined infectious students without necessitating test results. Targeted sampling had no substantial advantages over simple random sampling when the model incorporated contact tracing and app-based symptom monitoring but reduced the upper boundary on 90% prediction intervals for cumulative infections when either was removed. Thus, targeted sampling designs for surveillance testing may mitigate worst-case outcomes when other interventions are less effective. The results' implications for future EIDs are discussed.

Leveraging multiple data types to estimate the size of the Zika epidemic in the Americas.

Several hundred thousand Zika cases have been reported across the Americas since 2015. Incidence of infection was likely much higher, however, due to a high frequency of asymptomatic infection and other challenges that surveillance systems faced. Using a hierarchical Bayesian model with empirically-informed priors, we leveraged multiple types of Zika case data from 15 countries to estimate subnational reporting probabilities and infection attack rates (IARs). Zika IAR estimates ranged from 0.084 (95% CrI: 0.067-0.096) in Peru to 0.361 (95% CrI: 0.214-0.514) in Ecuador, with significant subnational variability in every country. Totaling infection estimates across these and 33 other countries and territories, our results suggest that 132.3 million (95% CrI: 111.3-170.2 million) people in the Americas had been infected by the end of 2018. These estimates represent the most extensive attempt to determine the size of the Zika epidemic in the Americas, offering a baseline for assessing the risk of future Zika epidemics in this region.

Local and regional dynamics of chikungunya virus transmission in Colombia: the role of mismatched spatial heterogeneity.

BACKGROUND: Mathematical models of transmission dynamics are routinely fitted to epidemiological time series, which must inevitably be aggregated at some spatial scale. Weekly case reports of chikungunya have been made available nationally for numerous countries in the Western Hemisphere since late 2013, and numerous models have made use of this data set for forecasting and inferential purposes. Motivated by an abundance of literature suggesting that the transmission of this mosquito-borne pathogen is localized at scales much finer than nationally, we fitted models at three different spatial scales to weekly case reports from Colombia to explore limitations of analyses of nationally aggregated time series data. METHODS: We adapted the recently developed Disease Transmission Kernel (DTK)-Dengue model for modeling chikungunya virus (CHIKV) transmission, given the numerous similarities of these viruses vectored by a common mosquito vector. We fitted versions of this model specified at different spatial scales to weekly case reports aggregated at different spatial scales: (1) single-patch national model fitted to national data; (2) single-patch departmental models fitted to departmental data; and (3) multi-patch departmental models fitted to departmental data, where the multiple patches refer to municipalities within a department. We compared the consistency of simulations from fitted models with empirical data. RESULTS: We found that model consistency with epidemic dynamics improved with increasing spatial granularity of the model. Specifically, the sum of single-patch departmental model fits better captured national-level temporal patterns than did a single-patch national model. Likewise, multi-patch departmental model fits better captured department-level temporal patterns than did single-patch departmental model fits. Furthermore, inferences about municipal-level incidence based on multi-patch departmental models fitted to department-level data were positively correlated with municipal-level data that were withheld from model fitting. CONCLUSIONS: Our model performed better when posed at finer spatial scales, due to better matching between human populations with locally relevant risk. Confronting spatially aggregated models with spatially aggregated data imposes a serious structural constraint on model behavior by averaging over epidemiologically meaningful spatial variation in drivers of transmission, impairing the ability of models to reproduce empirical patterns.

Characterization of Dengue Virus Infections Among Febrile Children Clinically Diagnosed With a Non-Dengue Illness, Managua, Nicaragua.

Background: We sought to characterize dengue virus (DENV) infections among febrile children enrolled in a pediatric cohort study who were clinically diagnosed with a non-dengue illness ("C cases"). Methods: DENV infections were detected and viral load quantitated by real-time reverse transcription-polymerase chain reaction in C cases presenting between January 2007 and January 2013. Results: One hundred forty-one of 2892 C cases (4.88%) tested positive for DENV. Of all febrile cases in the study, DENV-positive C cases accounted for an estimated 52.0% of patients with DENV viremia at presentation. Compared with previously detected, symptomatic dengue cases, DENV-positive C cases were significantly less likely to develop long-lasting humoral immune responses to DENV, as measured in healthy annual serum samples (79.7% vs 47.8%; P < .001). Humoral immunity was associated with viral load at presentation: 40 of 43 patients (93.0%) with a viral load ≥7.0 log10 copies/mL serum developed the expected rise in anti-DENV antibodies in annual samples versus 13 of 68 (19.1%) patients with a viral load below this level (P < .001). Conclusions: Antibody responses to DENV-positive C cases differ from responses to classic symptomatic dengue. These findings have important implications for DENV transmission modeling, immunology, and epidemiologic surveillance.

Viremia and Clinical Presentation in Nicaraguan Patients Infected With Zika Virus, Chikungunya Virus, and Dengue Virus.

BACKGROUND: Zika virus (ZIKV), chikungunya virus (CHIKV), and dengue virus (DENV) cocirculate in Nicaragua. In this study, we sought to compare the quantified viremia and clinical presentation of patients infected with 1 or more of these viruses. METHODS: Acute-phase serum samples from 346 patients with a suspected arboviral illness were tested using a multiplex real-time reverse-transcription polymerase chain reaction for ZIKV, CHIKV, and DENV. Viremia was quantitated for each detected virus, and clinical information from request forms submitted with each sample was recorded. RESULTS: A total of 263 patients tested positive for 1 or more viruses: 192 patients tested positive for a single virus (monoinfections) and 71 patients tested positive for 2 or all 3 viruses (coinfections). Quantifiable viremia was lower in ZIKV infections compared with CHIKV or DENV (mean 4.70 vs 6.42 and 5.84 log10 copies/mL serum, respectively; P < .001 for both comparisons), and for each virus, mean viremia was significantly lower in coinfections than in monoinfections. Compared with patients with CHIKV or DENV, ZIKV patients were more likely to have a rash (P < .001) and less likely to be febrile (P < .05) or require hospitalization (P < .001). Among all patients, hospitalized cases had higher viremia than those who did not require hospitalization (7.1 vs 4.1 log10 copies/mL serum, respectively; P < .001). CONCLUSIONS: ZIKV, CHIKV, and DENV result in similar clinical presentations, and coinfections may be relatively common. Our findings illustrate the need for accurate, multiplex diagnostics for patient care and epidemiologic surveillance.