scholarly journals Reconstructing the early global dynamics of under-ascertained COVID-19 cases and infections

2020 ◽  
Author(s):  
Timothy W Russell ◽  
Nick Golding ◽  
Joel Hellewell ◽  
Sam Abbott ◽  
Lawrence Wright ◽  
...  
Keyword(s):  
Process Model ◽  
Case Fatality ◽  
Sudden Onset ◽  
Control Measures ◽  
Global Dynamics ◽  
Median Percentage ◽  
Epidemic Dynamics ◽  
Response Planning ◽  
Seroprevalence Data ◽  
Reported Data

Background: Asymptomatic or subclinical SARS-CoV-2 infections are often unreported, which means that confirmed case counts may not accurately reflect underlying epidemic dynamics. Understanding the level of ascertainment (the ratio of confirmed symptomatic cases to the true number of symptomatic individuals) and undetected epidemic progression is crucial to informing COVID-19 response planning, including the introduction and relaxation of control measures. Estimating case ascertainment over time allows for accurate estimates of specific outcomes such as seroprevalence, which is essential for planning control measures. Methods: Using reported data on COVID-19 cases and fatalities globally, we estimated the proportion of symptomatic cases (i.e. any person with any of fever >= 37.5C, cough, shortness of breath, sudden onset of anosmia, ageusia or dysgeusia illness) that were reported in 210 countries and territories, given those countries had experienced more than ten deaths. We used published estimates of the baseline case fatality ratio (CFR), which was adjusted for delays and under-ascertainment, then calculated the ratio of this baseline CFR to an estimated local delay-adjusted CFR to estimate the level of under-ascertainment in a particular location. We then fit a Bayesian Gaussian process model to estimate the temporal pattern of under-ascertainment. Results: Based on reported cases and deaths, we estimated that, during March 2020, the median percentage of symptomatic cases detected across the 84 countries which experienced more than ten deaths ranged from 2.4% (Bangladesh) to 100% (Chile). Across the ten countries with the highest number of total confirmed cases as of 6th July 2020, we estimated that the peak number of symptomatic cases ranged from 1.4 times (Chile) to 18 times (France) larger than reported. Comparing our model with national and regional seroprevalence data where available, we find that our estimates are consistent with observed values. Finally, we estimated seroprevalence for each country. As of the 7th June, our seroprevalence estimates range from 0% (many countries) to 13% (95% CrI: 5.6% - 24%) (Belgium). Conclusions: We found substantial under-ascertainment of symptomatic cases, particularly at the peak of the first wave of the SARS-CoV-2 pandemic, in many countries. Reported case counts will therefore likely underestimate the rate of outbreak growth initially and underestimate the decline in the later stages of an epidemic. Although there was considerable under-reporting in many locations, our estimates were consistent with emerging serological data, suggesting that the proportion of each country's population infected with SARS-CoV-2 worldwide is generally low.

scholarly journals Reconstructing the early global dynamics of under-ascertained COVID-19 cases and infections

BMC Medicine ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Timothy W. Russell ◽  
◽  
Nick Golding ◽  
Joel Hellewell ◽  
Sam Abbott ◽  
...  
Keyword(s):  
Process Model ◽  
Case Fatality ◽  
Sudden Onset ◽  
Control Measures ◽  
Global Dynamics ◽  
Median Percentage ◽  
Epidemic Dynamics ◽  
Response Planning ◽  
Seroprevalence Data ◽  
Reported Data

Abstract Background Asymptomatic or subclinical SARS-CoV-2 infections are often unreported, which means that confirmed case counts may not accurately reflect underlying epidemic dynamics. Understanding the level of ascertainment (the ratio of confirmed symptomatic cases to the true number of symptomatic individuals) and undetected epidemic progression is crucial to informing COVID-19 response planning, including the introduction and relaxation of control measures. Estimating case ascertainment over time allows for accurate estimates of specific outcomes such as seroprevalence, which is essential for planning control measures. Methods Using reported data on COVID-19 cases and fatalities globally, we estimated the proportion of symptomatic cases (i.e. any person with any of fever ≥ 37.5 °C, cough, shortness of breath, sudden onset of anosmia, ageusia or dysgeusia illness) that were reported in 210 countries and territories, given those countries had experienced more than ten deaths. We used published estimates of the baseline case fatality ratio (CFR), which was adjusted for delays and under-ascertainment, then calculated the ratio of this baseline CFR to an estimated local delay-adjusted CFR to estimate the level of under-ascertainment in a particular location. We then fit a Bayesian Gaussian process model to estimate the temporal pattern of under-ascertainment. Results Based on reported cases and deaths, we estimated that, during March 2020, the median percentage of symptomatic cases detected across the 84 countries which experienced more than ten deaths ranged from 2.4% (Bangladesh) to 100% (Chile). Across the ten countries with the highest number of total confirmed cases as of 6 July 2020, we estimated that the peak number of symptomatic cases ranged from 1.4 times (Chile) to 18 times (France) larger than reported. Comparing our model with national and regional seroprevalence data where available, we find that our estimates are consistent with observed values. Finally, we estimated seroprevalence for each country. As of 7 June, our seroprevalence estimates range from 0% (many countries) to 13% (95% CrI 5.6–24%) (Belgium). Conclusions We found substantial under-ascertainment of symptomatic cases, particularly at the peak of the first wave of the SARS-CoV-2 pandemic, in many countries. Reported case counts will therefore likely underestimate the rate of outbreak growth initially and underestimate the decline in the later stages of an epidemic. Although there was considerable under-reporting in many locations, our estimates were consistent with emerging serological data, suggesting that the proportion of each country’s population infected with SARS-CoV-2 worldwide is generally low.

scholarly journals Data-Based Analysis, Modelling and Forecasting of the COVID-19 outbreak

2020 ◽  
Author(s):  
Cleo Anastassopoulou ◽  
Lucia Russo ◽  
Athanasios Tsakris ◽  
Constantinos Siettos
Keyword(s):  
Lower Bound ◽  
Total Population ◽  
Case Fatality ◽  
Epidemiological Data ◽  
Control Measures ◽  
Suspected Case ◽  
Cumulative Number ◽  
Case Fatality Ratio ◽  
Average Value ◽  
Reported Data

AbstractSince the first suspected case of coronavirus disease-2019 (COVID-19) on December 1st, 2019, in Wuhan, Hubei Province, China, a total of 40,235 confirmed cases and 909 deaths have been reported in China up to February 10, 2020, evoking fear locally and internationally. Here, based on the publicly available epidemiological data for Hubei, China from January 11 to February 10, 2020, we provide estimates of the main epidemiological parameters. In particular, we provide an estimation of the case fatality and case recovery ratios, along with their 90% confidence intervals as the outbreak evolves. On the basis of a Susceptible-Infected-Recovered-Dead (SIDR) model, we provide estimations of the basic reproduction number (R0), and the per day infection mortality and recovery rates. By calibrating the parameters of the SIRD model to the reported data, we also attempt to forecast the evolution of the of the outbreak at the epicenter three weeks ahead, i.e. until February 29. As the number of infected individuals, especially of those with asymptomatic or mild courses, is suspected to be much higher than the official numbers, which can be considered only as a subset of the actual numbers of infected and recovered cases in the total population, we have repeated the calculations under a second scenario that considers twenty times the number of confirmed infected cases and forty times the number of recovered, leaving the number of deaths unchanged. Based on the reported data, the expected value of R0 as computed considering the period from the 11th of January until the 18th of January, using the official counts of confirmed cases was found to be ∼4.6, while the one computed under the second scenario was found to be ∼3.2. Thus, based on the SIRD simulations, the estimated average value of R0 was found to be ∼ 2.6 based on confirmed cases and2 based on the second scenario. Our forecasting flashes a note of caution for the presently unfolding outbreak in China. Based on the official counts for confirmed cases, the simulations suggest that the cumulative number of infected could reach 180,000 (with lower bound of 45,000) by February 29. Regarding the number of deaths, simulations forecast that on the basis of the up to the 10th of February reported data, the death toll might exceed 2,700 (as a lower bound) by February 29. Our analysis further reveals a significant decline of the case fatality ratio from January 26 to which various factors may have contributed, such as the severe control measures taken in Hubei, China (e.g. quarantine and hospitalization of infected individuals), but mainly because of the fact that the actual cumulative numbers of infected and recovered cases in the population most likely are much higher than the reported ones. Thus, in a scenario where we have taken twenty times the confirmed number of infected and forty times the confirmed number of recovered cases, the case fatality ratio is around ∼ 0.15% in the total population. Importantly, based on this scenario, simulations suggest a slow down of the outbreak in Hubei at the end of February.

Influenza Surveillance Islamabad and Rawalpindi, Pakistan, September 2017 - February 2018 (Preprint)

2020 ◽  
Author(s):  
Dr Nadia ◽  
Saima Dil ◽  
Naveed Ullah Khan ◽  
Rana Jawad Asghar ◽  
Farida Khudaidad Khan ◽  
...  
Keyword(s):  
Health Care Providers ◽  
Influenza A ◽  
Case Fatality ◽  
Sudden Onset ◽  
Control Measures ◽  
Influenza Surveillance ◽  
Care Providers ◽  
Surveillance Network ◽  
Cross Sectional ◽  
Influenza A H1n1

BACKGROUND Background: Globally 5-10%adults and 20-30%children are affected by influenza annually. Annual epidemics result in 3-5million cases of serious illness and approximately500,000 deaths. In 2008 a sentinel lab-based influenza surveillance network was established in Pakistan in collaboration with CDC having objectives to assess trends of Influenza-like-Illness(ILI) and Severe Acute Respiratory Illness(SARI). OBJECTIVE Objectives: To assess burden of disease, identify risk factors, and recommend control measures. METHODS Methods: A cross-sectional study was conducted based on influenza surveillance data obtained from NICLP from September 2017 to February 2018.Study was done from the data records and samples of suspected ILI patients received from hospitals of Islamabad and Rawalpindi. A case was defined as sudden onset of fever of ≥ 38 C° and cough, with onset within last 10 days. Samples were tested at NICLP for confirmation by RT-PCR. Frequencies were calculated and data analyzed as per time, place and person RESULTS Results: A total of 1500 samples were received out of which 435(29%) were found positive. Among positive samples 246(56.5%) were Influenza-A(H1N1) pdm09,165(38%) were Influenza-A(H3N1) and 24(5.5%) were influenza B.Mean age was 39 years(range 40 days-80 years)while maximum cases were reported from age group 30-39 years(n=77)followed by 50-59 years(n=59).Males were predominant 256(58.8%). Among cases, 21(4.8%) healthcare workers. Travel history was found in 21(4.8%) cases while 35(8%) cases had contact with influenza patients and 14(3.2%) had contact with birds. Among positive cases 262(60%) were reported from Rawalpindi. Majority of cases were reported in January (277) followed by February (112). 31.4% met SARI case definition. Median hospital stay was 5days.During hospitalization 124(26.3%) were ICU admissions, out of them 2(0.42%) were on ventilator, 83(17.6%) were mechanically ventilated. Prevalence of influenza in reported cases was 0.01%.Six confirmed cases died with Case Fatality Rate=1.27%. CONCLUSIONS Conclusion Most cases reported were of Influenza-A (H1N1) pdm09. Based on the results, policy for inclusion of flu vaccination on annual basis is recommended for health care providers and general community.

scholarly journals Evaluating the effectiveness of measures to control the novel coronavirus disease 2019 in Jilin Province, China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qinglong Zhao ◽  
Yao Wang ◽  
Meng Yang ◽  
Meina Li ◽  
Zeyu Zhao ◽  
...  
Keyword(s):  
Prevention And Control ◽  
Good Control ◽  
Asymptomatic Infection ◽  
Jilin Province ◽  
Control Measures ◽  
First Case ◽  
Before And After ◽  
Novel Coronavirus ◽  
Reported Data ◽  
And Control

Abstract Background Based on differences in populations and prevention and control measures, the spread of new coronary pneumonia in different countries and regions also differs. This study aimed to calculate the transmissibility of coronavirus disease 2019 (COVID-19), and to evaluate the effectiveness of measures to control the disease in Jilin Province, China. Methods The data of reported COVID-19 cases were collected, including imported and local cases from Jilin Province as of March 14, 2019. A Susceptible–Exposed–Infectious–Asymptomatic–Recovered/Removed (SEIAR) model was developed to fit the data, and the effective reproduction number (Reff) was calculated at different stages in the province. Finally, the effectiveness of the measures was assessed. Results A total of 97 COVID-19 infections were reported in Jilin Province, among which 45 were imported infections (including one asymptomatic infection) and 52 were local infections (including three asymptomatic infections). The model fit the reported data well (R2 = 0.593, P < 0.001). The Reff of COVID-19 before and after February 1, 2020 was 1.64 and 0.05, respectively. Without the intervention taken on February 1, 2020, the predicted cases would have reached a peak of 177,011 on October 22, 2020 (284 days from the first case). The projected number of cases until the end of the outbreak (on October 9, 2021) would have been 17,129,367, with a total attack rate of 63.66%. Based on the comparison between the predicted incidence of the model and the actual incidence, the comprehensive intervention measures implemented in Jilin Province on February 1 reduced the incidence of cases by 99.99%. Therefore, according to the current measures and implementation efforts, Jilin Province can achieve good control of the virus’s spread. Conclusions COVID-19 has a moderate transmissibility in Jilin Province, China. The interventions implemented in the province had proven effective; increasing social distancing and a rapid response by the prevention and control system will help control the spread of the disease.

scholarly journals COVID-19 in Sudan

2021 ◽  
Vol 15 (02) ◽  
pp. 204-208
Author(s):  
Ayman Ahmed ◽  
Nouh Saad Mohamed ◽  
Sarah Misbah EL-Sadig ◽  
Lamis Ahmed Fahal ◽  
Ziad Bakri Abelrahim ◽  
...  
Keyword(s):  
Health Systems ◽  
Case Fatality Rate ◽  
Case Fatality ◽  
Control Measures ◽  
Religious Practices ◽  
Mass Gathering ◽  
Fatality Rate ◽  
High Case Fatality Rate ◽  
The Government ◽  
And Control

The steadily growing COVID-19 pandemic is challenging health systems worldwide including Sudan. In Sudan, the first COVID-19 case was reported on 13th March 2020, and up to 11 November 2020 there were 14,401 confirmed cases of which 9,535 cases recovered and the rest 3,750 cases were under treatment. Additionally, 1,116 deaths were reported, indicating a relatively high case fatality rate of 7.7%. Several preventive and control measures were implemented by the government of Sudan and health partners, including the partial lockdown of the country, promoting social distancing, and suspending mass gathering such as festivals and performing religious practices in groups. However, new cases still emerging every day and this could be attributed to the noncompliance of the individuals to the advocated preventive measurements.

scholarly journals Malaria Outbreak Facilitated by Appearance of Vector-Breeding Sites after Heavy Rainfall and Inadequate Preventive Measures: Nwoya District, Northern Uganda, February–May 2018

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Godfrey Nsereko ◽  
Daniel Kadobera ◽  
Denis Okethwangu ◽  
Joyce Nguna ◽  
Damian Rutazaana ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Case Control Study ◽  
Preventive Measures ◽  
Case Fatality ◽  
Case Control ◽  
Control Measures ◽  
Northern Uganda ◽  
Entire Study ◽  
Breeding Sites ◽  
Control Study

Background. Malaria is a leading cause of morbidity and mortality in Uganda. In April 2018, malaria cases surged in Nwoya District, Northern Uganda, exceeding expected limits and thereby requiring epidemic response. We investigated this outbreak to estimate its magnitude, identify exposure factors for transmission, and recommend evidence-based control measures. Methods. We defined a malaria case as onset of fever in a resident of Anaka subcounty, Koch Goma subcounty, and Nwoya Town Council, Nwoya District, with a positive rapid diagnostic test or microscopy for malaria from 1 February to 25 May 2018. We reviewed medical records in all health facilities of affected subcounties to find cases. In a case-control study, we compared exposure factors between case-persons and asymptomatic controls matched by age and village. We also conducted entomological assessments on vector density and behavior. Results. We identified 3,879 case-persons (attack rate [AR] = 6.5%) and two deaths (case-fatality rate = 5.2/10,000). Females (AR = 8.1%) were more affected than males (AR = 4.7%) (p<0.0001). Of all age groups, 5–18 years (AR = 8.4%) were most affected. Heavy rain started in early March 2018, and a propagated outbreak followed in the first week of April 2018. In the case-control study, 55% (59/107) of case-persons and 18% (19/107) of controls had stagnant water around households for several days following rainfall (ORM-H = 5.6, 95% CI = 3.0–11); 25% (27/107) of case-persons and 51% (55/107) of controls wore full extremity covering clothes during evening hours (ORM-H = 0.30, 95% CI = 0.20–0.60); 71% (76/107) of case-persons and 85% (91/107) of controls slept under a long-lasting insecticide-treated net (LLIN) 14 days before symptom onset (ORM-H = 0.43, 95% CI = 0.22–0.85); 37% (40/107) of case-persons and 52% (56/107) of controls had access to at least one LLIN per 2 household members (ORM-H = 0.54, 95% CI = 0.30–0.97). Entomological assessment indicated active breeding sites in the entire study area; Anopheles gambiae sensu lato species were the predominant vector. Conclusion. Increased vector-breeding sites after heavy rainfall and inadequate malaria preventive measures were found to have contributed to this outbreak. We recommended increasing coverage for LLINs and larviciding breeding sites in the area.

scholarly journals 2452. Outbreak of Candidemia Associated With a Contaminated Intravenous (IV) Anesthetic in an Adult Intensive Care Unit (ICU) in San Luis Potosí, México.

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S848-S848 ◽  
Author(s):  
Carlos Portales-Castillo ◽  
Javier Araujo-Meléndez ◽  
Pedro Torres-González ◽  
Mariana Mancilla-González
Keyword(s):  
Early Recognition ◽  
Case Fatality ◽  
Case Control ◽  
Fungal Species ◽  
Case Definition ◽  
Control Measures ◽  
Epidemiological Surveillance ◽  
Effective Control ◽  
Adult Intensive Care Unit ◽  
Outbreak Case

Abstract Background In June 2018, an unusual number of candidemia-associated sepsis cases were diagnosed in sedated patients hospitalized in the 12-bed adult ICU of a teaching hospital in Mexico. The pre-outbreak candidemia rate had been calculated at 0.66 cases/100 ICU admissions for the previous 3 years. Methods We performed a case–control and microbiological study designed to trace the source of the outbreak. Case definition included adult patients with systemic inflammatory response syndrome and Candida species isolated on BC (blood cultures). The rest of the patients in the ICU within the study period (6/12/2018–6/22/2018) were used as controls. Results A total of 5 cases and 19 controls were included in the study. Demographic and clinical characteristics were similar between groups, except for SOFA scores (Table 1). Differences in median SOFA scores between groups were statistically significant (7.5 in cases and 3 in controls (p = 0.02)). After review of common medications used between cases, propofol infusion use (5/5 in cases and 6/19 in controls) was calculated as the strongest risk factor for candidemia (OR 22.84 (p = 0.04)). In-use propofol infusions available at the time were stopped and sent for culture as were unopened vials stored in the pharmacy from the lot being used in the ICU. Intrinsical contamination with bacterial and fungal species related to the outbreak was identified (Table 3). Case fatality rate during the outbreak was 80% (4/5) Conclusion Lethal infections due to contaminated medications, including propofol, have been reported worldwide. Propofol is a potential source for infections given its lipophilic nature that promotes microbial growth. This likely remains an underecognized problem that deserves awareness for early recognition. Epidemiological surveillance in our hospital prompted our case–control study and the subsequent implementation of effective control measures including rapid notification to hospital and national authorities (COFEPRIS), elimination of the identified contaminated lot, and increased promotion of both hand hygiene and adequate IV medication handling techniques among staff. Disclosures All authors: No reported disclosures.

scholarly journals Experiential learnings from the Nipah virus outbreaks in Kerala towards containment of infectious public health emergencies in India

2020 ◽  
Vol 148 ◽  
Author(s):  
Rima R. Sahay ◽  
Pragya D. Yadav ◽  
Nivedita Gupta ◽  
Anita M. Shete ◽  
Chandni Radhakrishnan ◽  
...  
Keyword(s):  
State Government ◽  
Single Case ◽  
Point Of Care ◽  
Nipah Virus ◽  
Case Fatality ◽  
Control Measures ◽  
Contact Tracing ◽  
Effective Control ◽  
Hospital Infection Control ◽  
Kerala State

Abstract Nipah virus (NiV) outbreak occurred in Kozhikode district, Kerala, India in 2018 with a case fatality rate of 91% (21/23). In 2019, a single case with full recovery occurred in Ernakulam district. We described the response and control measures by the Indian Council of Medical Research and Kerala State Government for the 2019 NiV outbreak. The establishment of Point of Care assays and monoclonal antibodies administration facility for early diagnosis, response and treatment, intensified contact tracing activities, bio-risk management and hospital infection control training of healthcare workers contributed to effective control and containment of NiV outbreak in Ernakulam.

scholarly journals Modeling the Novel Coronavirus (SARS-CoV-2) Outbreak in Sicily, Italy

2020 ◽  
Vol 17 (14) ◽  
pp. 4964 ◽  
Author(s):  
Andrea Maugeri ◽  
Martina Barchitta ◽  
Sebastiano Battiato ◽  
Antonella Agodi
Keyword(s):  
Transmission Rate ◽  
Control Measures ◽  
Transmission Dynamics ◽  
The Novel ◽  
Epidemic Spread ◽  
Transmission Rates ◽  
Contact Precautions ◽  
Travel Restrictions ◽  
Novel Coronavirus ◽  
Reported Data

Italy was the first country in Europe which imposed control measures of travel restrictions, quarantine and contact precautions to tackle the epidemic spread of the novel coronavirus (SARS-CoV-2) in all its regions. While such efforts are still ongoing, uncertainties regarding SARS-CoV-2 transmissibility and ascertainment of cases make it difficult to evaluate the effectiveness of restrictions. Here, we employed a Susceptible-Exposed-Infectious-Recovered-Dead (SEIRD) model to assess SARS-CoV-2 transmission dynamics, working on the number of reported patients in intensive care unit (ICU) and deaths in Sicily (Italy), from 24 February to 13 April. Overall, we obtained a good fit between estimated and reported data, with a fraction of unreported SARS-CoV-2 cases (18.4%; 95%CI = 0–34.0%) before 10 March lockdown. Interestingly, we estimated that transmission rate in the community was reduced by 32% (95%CI = 23–42%) after the first set of restrictions, and by 80% (95%CI = 70–89%) after those adopted on 23 March. Thus, our estimates delineated the characteristics of SARS-CoV2 epidemic before restrictions taking into account unreported data. Moreover, our findings suggested that transmission rates were reduced after the adoption of control measures. However, we cannot evaluate whether part of this reduction might be attributable to other unmeasured factors, and hence further research and more accurate data are needed to understand the extent to which restrictions contributed to the epidemic control.

scholarly journals Integrating serological and genetic data to quantify cross-species transmission: brucellosis as a case study

Parasitology ◽  
2016 ◽  
Vol 143 (7) ◽  
pp. 821-834 ◽  
Author(s):  
MAFALDA VIANA ◽  
GABRIEL M. SHIRIMA ◽  
KUNDA S. JOHN ◽  
JULIE FITZPATRICK ◽  
RUDOVICK R. KAZWALA ◽  
...  
Keyword(s):  
Process Model ◽  
Epidemiological Data ◽  
Human Infection ◽  
Control Measures ◽  
Transmission Dynamics ◽  
Northern Tanzania ◽  
Animal Infection ◽  
Latent Process ◽  
Level Of Understanding

SUMMARYEpidemiological data are often fragmented, partial, and/or ambiguous and unable to yield the desired level of understanding of infectious disease dynamics to adequately inform control measures. Here, we show how the information contained in widely available serology data can be enhanced by integration with less common type-specific data, to improve the understanding of the transmission dynamics of complex multi-species pathogens and host communities. Using brucellosis in northern Tanzania as a case study, we developed a latent process model based on serology data obtained from the field, to reconstruct Brucella transmission dynamics. We were able to identify sheep and goats as a more likely source of human and animal infection than cattle; however, the highly cross-reactive nature of Brucella spp. meant that it was not possible to determine which Brucella species (B. abortus or B. melitensis) is responsible for human infection. We extended our model to integrate simulated serology and typing data, and show that although serology alone can identify the host source of human infection under certain restrictive conditions, the integration of even small amounts (5%) of typing data can improve understanding of complex epidemiological dynamics. We show that data integration will often be essential when more than one pathogen is present and when the distinction between exposed and infectious individuals is not clear from serology data. With increasing epidemiological complexity, serology data become less informative. However, we show how this weakness can be mitigated by integrating such data with typing data, thereby enhancing the inference from these data and improving understanding of the underlying dynamics.

Sign in / Sign up

Export Citation Format

Share Document