Modeling geographic vaccination strategies for COVID-19 in Norway.

Vaccination was a key intervention in controlling the COVID-19 pandemic globally. In early 2021, Norway faced significant regional variations in COVID-19 incidence and prevalence, with large differences in population density, necessitating efficient vaccine allocation to reduce infections and severe outcomes. This study explored alternative vaccination strategies to minimize health outcomes (infections, hospitalizations, ICU admissions, deaths) by varying regions prioritized, extra doses prioritized, and implementation start time. Using two models (individual-based and meta-population), we simulated COVID-19 transmission during the primary vaccination period in Norway, covering the first 7 months of 2021. We investigated alternative strategies to allocate more vaccine doses to regions with a higher force of infection. We also examined the robustness of our results and highlighted potential structural differences between the two models. Our findings suggest that early vaccine prioritization could reduce COVID-19 related health outcomes by 8% to 20% compared to a baseline strategy without geographic prioritization. For minimizing infections, hospitalizations, or ICU admissions, the best strategy was to initially allocate all available vaccine doses to fewer high-risk municipalities, comprising approximately one-fourth of the population. For minimizing deaths, a moderate level of geographic prioritization, with approximately one-third of the population receiving doubled doses, gave the best outcomes by balancing the trade-off between vaccinating younger people in high-risk areas and older people in low-risk areas. The actual strategy implemented in Norway was a two-step moderate level aimed at maintaining the balance and ensuring ethical considerations and public trust. However, it did not offer significant advantages over the baseline strategy without geographic prioritization. Earlier implementation of geographic prioritization could have more effectively addressed the main wave of infections, substantially reducing the national burden of the pandemic.

A real-time regional model for COVID-19: Probabilistic situational awareness and forecasting.

The COVID-19 pandemic is challenging nations with devastating health and economic consequences. The spread of the disease has revealed major geographical heterogeneity because of regionally varying individual behaviour and mobility patterns, unequal meteorological conditions, diverse viral variants, and locally implemented non-pharmaceutical interventions and vaccination roll-out. To support national and regional authorities in surveilling and controlling the pandemic in real-time as it unfolds, we here develop a new regional mathematical and statistical model. The model, which has been in use in Norway during the first two years of the pandemic, is informed by real-time mobility estimates from mobile phone data and laboratory-confirmed case and hospitalisation incidence. To estimate regional and time-varying transmissibility, case detection probabilities, and missed imported cases, we developed a novel sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, enabling policy-relevant situational awareness at a local scale. By comparing the reproduction numbers before and after lockdowns, we identify spatially heterogeneous patterns in their effect on the transmissibility, with a stronger effect in the most populated regions compared to the national reduction estimated to be 85% (95% CI 78%-89%). Our approach is the first regional changepoint stochastic metapopulation model capable of real time spatially refined surveillance and forecasting during emergencies.

A One Health real-time surveillance system for nowcasting Campylobacter gastrointestinal illness outbreaks, Norway, week 30 2010 to week 11 2022.

BackgroundCampylobacter is a leading cause of food and waterborne illness. Monitoring and modelling Campylobacter at chicken broiler farms, combined with weather pattern surveillance, can aid nowcasting of human gastrointestinal (GI) illness outbreaks. Near real-time sharing of data and model results with health authorities can help increase potential outbreak responsiveness.AimsTo leverage data on weather and Campylobacter on broiler farms to build a risk model for possible human Campylobacter outbreaks and to communicate risk assessments with health authorities.MethodsWe developed a spatio-temporal random effects model for weekly GI illness consultations in Norwegian municipalities with Campylobacter monitoring and weather data from week 30 2010 to 11 2022 to give 1-week nowcasts of GI illness outbreaks. The approach combined a municipality random effects baseline model for seasonally-adjusted GI illness with a second model for peak deviations from that baseline. Model results are communicated to national and local stakeholders through an interactive website: Sykdomspulsen One Health.ResultsLagged temperature and precipitation covariates, as well as 2-week-lagged positive Campylobacter sampling in broilers, were associated with higher levels of GI consultations. Significant inter-municipality variability in outbreak nowcasts were observed.ConclusionsCampylobacter surveillance in broilers can be useful in GI illness outbreak nowcasting. Surveillance of Campylobacter along potential pathways from the environment to illness such as via water system monitoring may improve nowcasting. A One Health system that communicates near real-time surveillance data and nowcast changes in risk to health professionals facilitates the prevention of Campylobacter outbreaks and reduces impact on human health.

Age and product dependent vaccine effectiveness against SARS-CoV-2 infection and hospitalisation among adults in Norway: a national cohort study, July-November 2021.

BACKGROUND: COVID-19 vaccines have been crucial in the pandemic response and understanding changes in vaccines effectiveness is essential to guide vaccine policies. Although the Delta variant is no longer dominant, understanding vaccine effectiveness properties will provide essential knowledge to comprehend the development of the pandemic and estimate potential changes over time. METHODS: In this population-based cohort study, we estimated the vaccine effectiveness of Comirnaty (Pfizer/BioNTech; BNT162b2), Spikevax (Moderna; mRNA-1273), Vaxzevria (AstraZeneca; ChAdOx nCoV-19; AZD1222), or a combination against SARS-CoV-2 infections, hospitalisations, intensive care admissions, and death using Cox proportional hazard models, across different vaccine product regimens and age groups, between 15 July and 31 November 2021 (Delta variant period). Vaccine status is included as a time-varying covariate and all models were adjusted for age, sex, comorbidities, county of residence, country of birth, and living conditions. Data from the entire adult Norwegian population were collated from the National Preparedness Register for COVID-19 (Beredt C19). RESULTS: The overall adjusted vaccine effectiveness against infection decreased from 81.3% (confidence interval (CI): 80.7 to 81.9) in the first 2 to 9 weeks after receiving a second dose to 8.6% (CI: 4.0 to 13.1) after more than 33 weeks, compared to 98.6% (CI: 97.5 to 99.2) and 66.6% (CI: 57.9 to 73.6) against hospitalisation respectively. After the third dose (booster), the effectiveness was 75.9% (CI: 73.4 to 78.1) against infection and 95.0% (CI: 92.6 to 96.6) against hospitalisation. Spikevax or a combination of mRNA products provided the highest protection, but the vaccine effectiveness decreased with time since vaccination for all vaccine regimens. CONCLUSIONS: Even though the vaccine effectiveness against infection waned over time, all vaccine regimens remained effective against hospitalisation after the second vaccine dose. For all vaccine regimens, a booster facilitated recovery of effectiveness. The results from this support the use of heterologous schedules, increasing flexibility in vaccination policy.

Increased household transmission and immune escape of the SARS-CoV-2 Omicron compared to Delta variants.

Understanding the epidemic growth of the novel SARS-CoV-2 Omicron variant is critical for public health. We compared the ten-day secondary attack rate (SAR) of the Omicron and Delta variants in households using Norwegian contact tracing data, December 2021 - January 2022. Omicron SAR was higher than Delta, with a relative risk (RR) of 1.41 (95% CI 1.27-1.56). We observed increased susceptibility to Omicron infection in household contacts compared to Delta, independent of contacts' vaccination status. Among three-dose vaccinated contacts, the mean SAR was lower for both variants. We found increased Omicron transmissibility from primary cases to contacts in all vaccination groups, except 1-dose vaccinated, compared to Delta. Omicron SAR of three-dose vaccinated primary cases was high, 46% vs 11 % for Delta. In conclusion, three-dose vaccinated primary cases with Omicron infection can efficiently spread in households, while three-dose vaccinated contacts have a lower risk of being infected by Delta and Omicron.

Simulation and identification of foodborne outbreaks in a large supermarket consumer purchase dataset.

Foodborne outbreaks represent a significant public health burden. Outbreak investigations are often challenging and time-consuming, and most outbreak vehicles remain unidentified. The development of alternative investigative strategies is therefore needed. Automated analysis of Consumer Purchase Data (CPD) gathered by retailers represents one such alternative strategy. CPD-aided investigations do not require trawling questionnaires to create a hypothesis and can provide analytical measures of association by direct data analysis. Here, we used anonymized CPD from 920,384 customers enrolled in Norway's largest supermarket loyalty program to simulate foodborne outbreaks across a range of different parameters and scenarios. We then applied a logistic regression model to calculate an odds ratio for each of the different possible food vehicles. By this method, we were able to identify outbreak vehicles with a 90% accuracy within a median of 6 recorded case-patients. The outbreak vehicle identification rate declined significantly when using data from only one of two retailers involved in a simulated outbreak. Performance was also reduced in simulations that restricted analysis from product ID to the product group levels accessible by trawling questionnaires. Our results show that-assuming agreements are in place with major retailers-CPD collection and analysis can solve foodborne outbreaks originating from supermarkets both more rapidly and accurately than than questionnaire-based methods and might provide a significant enhancement to current outbreak investigation methods.

Prevalence of antibodies against SARS-CoV-2 in the Norwegian population, August 2021.

BACKGROUND: One year into the COVID-19 pandemic, the cumulative number of confirmed COVID-19 cases in Norway was still low. In January 2021, when the Norwegian COVID-19 vaccination campaign started, the national seroprevalence estimate of SARS-CoV-2 antibodies was 3.2%. We have conducted a nationwide cross-sectional study in August 2021 to investigate the overall prevalence of SARS-CoV-2 antibodies in Norway after 8 months of COVID-19 mass vaccination and a third wave of SARS-CoV-2 infection. METHODS: Residual sera were collected from laboratories across Norway in August 2021. In IgG antibodies against the spike protein, the spike receptor binding domain (RBD) and the nucleocapsid protein of SARS-CoV-2 were measured by a bead-based flow cytometric assay. RESULTS: In total, 1926 residual sera were collected from individuals aged 0-98 years; 55.1% were from women. The overall national estimated seroprevalence from vaccination and/or infection was 62.6% (credible interval [CrI] 60.1%-65.2%) based on having antibodies against both spike and RBD. Estimated seroprevalence increased with age. Among all samples, 11.7% had antibodies against nucleocapsid. For unvaccinated children <12 years, the seroprevalence estimate due to SARS-CoV-2 infection was 12.5% (95% CrI 9.3%-16.1%). Of seropositive samples from the unvaccinated children, 31.9% lacked anti-nucleocapsid antibodies. CONCLUSIONS: The high overall SARS-CoV-2 seroprevalence estimates are in line with Norwegian registry data. Vaccination, not infection, contributed the most to the high seroprevalence in August 2021. Lack of antibodies against nucleocapsid should not automatically be interpreted as absence of previous infection as this could lead to underestimation of COVID-19 cases in seroprevalence studies.

A compelling demonstration of why traditional statistical regression models cannot be used to identify risk factors from case data on infectious diseases: a simulation study.

BACKGROUND: Regression models are often used to explain the relative risk of infectious diseases among groups. For example, overrepresentation of immigrants among COVID-19 cases has been found in multiple countries. Several studies apply regression models to investigate whether different risk factors can explain this overrepresentation among immigrants without considering dependence between the cases. METHODS: We study the appropriateness of traditional statistical regression methods for identifying risk factors for infectious diseases, by a simulation study. We model infectious disease spread by a simple, population-structured version of an SIR (susceptible-infected-recovered)-model, which is one of the most famous and well-established models for infectious disease spread. The population is thus divided into different sub-groups. We vary the contact structure between the sub-groups of the population. We analyse the relation between individual-level risk of infection and group-level relative risk. We analyse whether Poisson regression estimators can capture the true, underlying parameters of transmission. We assess both the quantitative and qualitative accuracy of the estimated regression coefficients. RESULTS: We illustrate that there is no clear relationship between differences in individual characteristics and group-level overrepresentation -small differences on the individual level can result in arbitrarily high overrepresentation. We demonstrate that individual risk of infection cannot be properly defined without simultaneous specification of the infection level of the population. We argue that the estimated regression coefficients are not interpretable and show that it is not possible to adjust for other variables by standard regression methods. Finally, we illustrate that regression models can result in the significance of variables unrelated to infection risk in the constructed simulation example (e.g. ethnicity), particularly when a large proportion of contacts is within the same group. CONCLUSIONS: Traditional regression models which are valid for modelling risk between groups for non-communicable diseases are not valid for infectious diseases. By applying such methods to identify risk factors of infectious diseases, one risks ending up with wrong conclusions. Output from such analyses should therefore be treated with great caution.

Reduced risk of hospitalisation among reported COVID-19 cases infected with the SARS-CoV-2 Omicron BA.1 variant compared with the Delta variant, Norway, December 2021 to January 2022.

We included 39,524 COVID-19 Omicron and 51,481 Delta cases reported in Norway from December 2021 to January 2022. We estimated a 73% reduced risk of hospitalisation (adjusted hazard ratio: 0.27; 95% confidence interval: 0.20-0.36) for Omicron compared with Delta. Compared with unvaccinated groups, Omicron cases who had completed primary two-dose vaccination 7-179 days before diagnosis had a lower reduced risk than Delta (66% vs 93%). People vaccinated with three doses had a similar risk reduction (86% vs 88%).

Increased transmissibility of the alpha SARS-CoV-2 variant: evidence from contact tracing data in Oslo, January to February 2021.

BACKGROUND: Information about the contagiousness of new SARS-CoV-2 variants, including the alpha lineage, and how they spread in various locations is essential. Country-specific estimates are needed because local interventions influence transmissibility. METHODS: We analysed contact tracing data from Oslo municipality, reported from January through February 2021, when the alpha lineage became predominant in Norway and estimated the relative transmissibility of the alpha lineage with the use of Poisson regression. RESULTS: Within households, we found an increase in the secondary attack rate by 60% (95% CI 20-114%) among cases infected with the alpha lineage compared to other variants; including all close contacts, the relative increase in the secondary attack rate was 24% (95% CI -6%-43%). There was a significantly higher risk of infecting household members in index cases aged 40-59 years who were infected with the alpha lineage; we found no association between transmission and household size. Overall, including all close contacts, we found that the reproduction number among cases with the alpha lineage was increased by 24% (95% CI 0%-52%), corresponding to an absolute increase of 0.19, compared to the group of index cases infected with other variants. CONCLUSION: Our study suggests that households are the primary locations for rapid transmission of the new lineage alpha.

Trends in seroprevalence of SARS-CoV-2 and infection fatality rate in the Norwegian population through the first year of the COVID-19 pandemic.

BACKGROUND: Infection with the novel coronavirus SARS-CoV-2 induces antibodies that can be used as a proxy for COVID-19. We present a repeated nationwide cross-sectional study assessing the seroprevalence of SARS-CoV-2, the infection fatality rate (IFR), and infection hospitalization rate (IHR) during the first year of the pandemic in Norway. METHODS: Residual serum samples were solicited in April/May 2020 (Round 1), in July/August 2020 (Round 2) and in January 2021 (Round 3). Antibodies against SARS-CoV-2 were measured using a flow cytometer-based assay. Aggregate data on confirmed cases, COVID-19-associated deaths and hospitalizations were obtained from the Emergency preparedness registry for COVID-19 (Beredt C19), and the seroprevalence estimates were used to estimate IFR and IHR. RESULTS: Antibodies against SARS-CoV-2 were measured in 4840 samples. The estimated seroprevalence increased from 0.8% (95% credible interval [CrI] 0.4%-1.3%) after the first wave of the pandemic (Rounds 1 and 2 combined) to 3.2% (95% CrI 2.3%-4.2%) (Round 3). The IFR and IHR were higher in the first wave than in the second wave and increased with age. The IFR was 0.2% (95% CrI 0.1%-0.3%), and IHR was 0.9% (95% CrI 0.6%-1.5%) for the second wave. CONCLUSIONS: The seroprevalence estimates show a cumulative increase of SARS-CoV-2 infections over time in the Norwegian population and suggest some under-recording of confirmed cases. The IFR and IHR were low, corresponding to the relatively low number of COVID-19-associated deaths and hospitalizations in Norway. Most of the Norwegian population was still susceptible to SARS-CoV-2 infection after the first year of the pandemic.

Cutaneous leishmaniasis among Syrian refugees in Jordan.

INTRODUCTION: Old World cutaneous leishmaniasis (CL) is one of the most prevalent vector-borne diseases within the World Health Organization's Eastern Mediterranean Region. The conflict in the Syrian Arab Republic generated large population movements and raised concerns about spreading of CL to countries where Syrians have relocated, including Jordan. METHODS: A review of electronic and paper-based registries of CL cases in Jordan was conducted to assess burden of disease and associated socio-demographic factors. RESULTS: Increasing numbers of CL cases have been reported in Jordan between 2010 and 2016 (from 140 to 281), paralleled by significant increases in incidence rate (from 2.09 per 100 000 person-years, 95% CI (1.77-2.47), to 2.87 per 100 000 person-year, 95% CI (2.55-3.22), p = 0.002) and in the proportion of cases who are Syrian (from 8.6%-55.2%, p < 0.001). Syrian refugees have higher odds of presenting with leishmaniasis than Jordanian nationals (adjusted OR 7.1, 95% CI (6.3-8.0), p = 0.000). CONCLUSIONS: Presence of large numbers of Syrian refugees within Jordan has so far not contributed to increased risk of developing CL for Jordanians, however surveillance, diagnosis and case management for CL should be reinforced to meet the increased burden. Electronic surveillance can help identify priority populations and areas for interventions.

Perceptions of use of a case-based, integrated national electronic public health surveillance system in Jordan.

Collection of real-time, standardized data remains a challenge for public health surveillance systems. The use of mobile information technology may facilitate this. A national case-based public health surveillance system was introduced in Jordan in 2015 using mobile tablets and an online framework. After training on the system, users were surveyed about their perceptions of it. Of 596 participants attending the training, 580 (97.3%) completed the survey. The majority of users were nurses (58.5%). Overall perceptions of the system were highly positive across 5 areas of functionality (standardized case definitions, clinical guidance on signs and symptoms, risk factors and laboratory guidance, SMS and Email alerts for notifiable diseases, one-hour reporting of information via an online framework). In all areas, over 80% of participants thought the system would help their work and would save time in identifying notifiable diseases and reporting this information centrally. Further work is encouraged to evaluate the system and consider the application of cloud-based models in other settings.

Design and implementation of a national public health surveillance system in Jordan.

Understanding and improving the health status of communities depend on effective public health surveillance. Adoption of new technologies, standardised case definitions and clinical guidelines for accurate diagnosis, and access to timely and reliable data, remains a challenge for public health surveillance systems however and existing public health surveillance systems are often fragmented, disease specific, inconsistent and of poor quality. We describe the application of an enterprise architecture approach to the design, planning and implementation of a national public health surveillance system in Jordan. This enabled a well planned and collaboratively supported system to be built and implemented using consistent standards for data collection, management, reporting and use. The system is case-based and integrated and employs mobile information technology to aid collection of real-time, standardised data to inform and improve decision-making at different levels of the health system.

Implementation of a Cloud-Based Electronic Medical Record to Reduce Gaps in the HIV Treatment Continuum in Rural Kenya.

BACKGROUND: Electronic medical record (EMR) systems are increasingly being adopted to support the delivery of health care in developing countries and their implementation can help to strengthen pathways of care and close gaps in the HIV treatment cascade by improving access to and use of data to inform clinical and public health decision-making. METHODS: This study implemented a novel cloud-based electronic medical record system in an HIV outpatient setting in Western Kenya and evaluated its impact on reducing gaps in the HIV treatment continuum including missing data and patient eligibility for ART. The impact of the system was assessed using a two-sample test of proportions pre- and post-implementation of EMR-based data verification and clinical decision support. RESULTS: Significant improvements in data quality and provision of clinical care were recorded through implementation of the EMR system, helping to ensure patients who are eligible for HIV treatment receive it early. A total of 2,169 and 764 patient records had missing data pre-implementation and post-implementation of EMR-based data verification and clinical decision support respectively. A total of 1,346 patients were eligible for ART, but not yet started on ART, pre-implementation compared to 270 patients pre-implementation. CONCLUSION: EMR-based data verification and clinical decision support can reduce gaps in HIV care, including missing data and eligibility for ART. A cloud-based model of EMR implementation removes the need for local clinic infrastructure and has the potential to enhance data sharing at different levels of health care to inform clinical and public health decision-making. A number of issues, including data management and patient confidentiality, must be considered but significant improvements in data quality and provision of clinical care are recorded through implementation of this EMR model.

Implementation of a cloud-based electronic medical record for maternal and child health in rural Kenya.

BACKGROUND: Complete and timely health information is essential to inform public health decision-making for maternal and child health, but is often lacking in resource-constrained settings. Electronic medical record (EMR) systems are increasingly being adopted to support the delivery of health care, and are particularly amenable to maternal and child health services. An EMR system could enable the mother and child to be tracked and monitored throughout maternity shared care, improve quality and completeness of data collected and enhance sharing of health information between outpatient clinic and the hospital, and between clinical and public health services to inform decision-making. METHODS: This study implemented a novel cloud-based electronic medical record system in a maternal and child health outpatient setting in Western Kenya between April and June 2013 and evaluated its impact on improving completeness of data collected by clinical and public health services. The impact of the system was assessed using a two-sample test of proportions pre- and post-implementation of EMR-based data verification. RESULTS: Significant improvements in completeness of the antenatal record were recorded through implementation of EMR-based data verification. A difference of 42.9% in missing data (including screening for hypertension, tuberculosis, malaria, HIV status or ART status of HIV positive women) was recorded pre- and post-implementation. Despite significant impact of EMR-based data verification on data completeness, overall screening rates in antenatal care were low. CONCLUSION: This study has shown that EMR-based data verification can improve the completeness of data collected in the patient record for maternal and child health. A number of issues, including data management and patient confidentiality, must be considered but significant improvements in data quality are recorded through implementation of this EMR model.

Stage of HIV presentation at initial clinic visit following a community-based HIV testing campaign in rural Kenya.

BACKGROUND: The Kenyan Ministry of Health and partners implemented a community-based integrated prevention campaign (IPC) in Western Kenya in 2008. The aim of this study was to determine whether the IPC, compared to Voluntary Counselling and Testing (VCT) services, was able to identify HIV positive individuals earlier in the clinical course of HIV infection following testing. METHODS: A total of 1,752 adults aged over 15 years who tested HIV positive through VCT services or the IPC, and subsequently registered at initial clinic visit between September 2008 and September 2010, were considered in the analysis. Multivariable logistic regression models were developed to assess the association of CD4 count and WHO clinical stage of HIV infection at first clinic appointment with age group, gender, marital status and HIV testing source. RESULTS: Male gender and marital status were independently associated with late HIV presentation (WHO clinical stage 3 or 4 or CD4 count ≤ 350 cells/µl) at initial clinic visit. Patients testing HIV positive during the IPC had significantly higher mean CD4 count at initial clinic visit compared to individuals who tested HIV positive via VCT services. Patients testing HIV positive during the IPC had more than two times higher odds of presenting early with CD4 count greater than 350 cells/µl (adjusted OR 2.15, 95% CI 1.28 - 3.61, p = 0.004) and presenting early with WHO clinical stage 1 or 2 of HIV infection (adjusted OR 2.39, 95% CI 1.24 - 4.60, p = 0.01) at initial clinic visit compared to individuals who tested HIV positive via VCT services. CONCLUSION: The community-based integrated prevention campaign identified HIV positive individuals earlier in the course of HIV infection, compared to Voluntary Counselling and Testing services. Community-based campaigns, such as the IPC, may be able to assist countries to achieve earlier testing and initiation of ART in the course of HIV infection. Improving referral mechanisms and strengthening linkages between HIV testing and treatment services remain a challenge and electronic medical record (EMR) systems may support monitoring of patients throughout the HIV care and treatment continuum.