This dataset is no longer being updated as of 5/11/2023. It is being retained on the Open Data Portal for its potential historical interest.

COVID-19 cases in the City of Cambridge as reported by the Cambridge Public Health Department. This dataset summarizes cases by age cohort and gender. Cambridge Public Health Department (CPHD) data regarding case counts come directly from MDPH and their surveillance system (MAVEN). We use the language and terminology per the Centers for Disease Control and Prevention (CDC) guidance, and recent guidance categorizes all confirmed or presumptive positive cases as positive. CPHD understands that case counts may be higher, due to a number of factors:

(1) Testing capabilities have been increased by other area lab organizations and hospitals and not all of the test results are reported to MDPH or CPHD.

(2) People may be asymptomatic (or have very mild symptoms) and do not realize that they may have COVID-19 and need to be tested.

(3) Providers may be offering diagnoses based on symptoms and history (rather than testing) and telling patients that they are likely positive and should stay home and self-quarantine for 14 days. (e.g, Someone might call their provider with a few symptoms and indicate that they were with someone from Biogen. In this case, the doctor may assume positive for COVID-19 and ask the patient to self-isolate at home for two weeks).

This is the process that the CPHD, and all local health departments, is following.

Total positive cases include total deaths. Any count <5 will be suppressed for privacy reasons

IntroductionIn December 2019, several cases of pneumonia of an unknown origin appeared in China. Previously, in that same year, the World Health Organization (WHO) had already published a list of the ten major global health issues which included the risk of a pandemic from respiratory diseases [1]. Later, in January 2020, the cause of the pneumonia cases detected in China was identified as being a new coronavirus of the Severe Acute Respiratory Syndrome (SARS-CoV-2) [2].The first records of SARS-CoV-2 infection, identified in Wuhan, China, spread quickly causing the territorial spread of contagions across dozens of countries. This lead the World Health Organization (WHO) to declare a pandemic, at the time more than 100 thousand cases of infection had already been detected in 114 countries and a total number of deaths higher than 4.000 [3].Geographical analysis of diseases are common in scientific literature [4, 5, 6] and Geographic Information Systems (GIS) and Spatial Analysis techniques have proven to be useful for studying how they spread across space and time [7, 8]. The spatial dimension plays a key role in epidemiological studies partly due to the growing development of technologies in terms of algorithms and processing capacity that allows the modeling of epidemiological phenomena [8]. COVID-19 studies GIS-based are just as important to understand unknown attributes of the disease in this time of great uncertainty, although, only a few studies have focused on geographic hotspots analysis and have tried to unveil the community drivers associated with the spatial patterns of local transmission [9].ObjectivesThis applied study is twofold. First seeks to highlight the importance of geographical factors in the current context; and second, uses geographic analysis methods and techniques, especially spatial statistic methods, to create evidence-based knowledge upon COVID-19 spatial spread, as well as its patterns and trends.In this way, ArcGIS Pro, Esri’s GIS software, is used in a space-time approach to synthetize the most relevant spatial dynamics. The specific objectives of the study are:1. Analyze the spatial patterns of the pandemic diffusion;2. Identify important transmission clusters;3. Identify spatial determinants of the disease spread.Study Area and DataThe study area is mainland Portugal at a municipal scale, due to being the finest scale of analysis with epidemiological information available in the official reports of the Direção-Geral da Saúde (DGS). Portugal has been severely affected by the pandemic and various spatial dynamics can be identified through time, since the patterns of incidence have changed in successive waves. In this way, the study is focused on various moments during the first year of incidence of the disease, capturing the most important patterns, tendencies and processes. Data used for this analysis is the epidemiological information of DGS [10], for the epidemiologic dimension, and Instituto Nacional de Estatística (INE) database [11] and Carta Social [12] for the variables that will be used as independent variables grouped in 3 dimensions: economic, sociodemographic and mobility (Figure 1).Figure 1 - Variables and respective dimensions of analysisMethodologyThe methodology is divided in 2 parts (Figure 2): the first is related to data acquisition, editing, management and integration in GIS, and the second is in relation to the modeling itself, in order to respond to the objectives which comprises of 3 phases: (i) space-time analysis of confirmed cases of infections to understand the diffusion processes; (ii) analysis of hot spots, clusters and outliers to identify the different patterns and tendencies over time and (iii) ordinary least squares regression (OLS) to identify the most important determinant spatial factors and drivers of the virus propagation.Figure 2 - Methodology flowResultsResults demonstrate an initial tendency of a hierarchical diffusion process, from centers of larger population densities to those of which are less dense (Figure 3), which is replaced and dominated in following periods by contagion expansion. Geographically, the first confirmed cases appeared in coastal cities and progressively penetrated into the interior of the country with a strong spatial association with the main roads and the population size of the territorial units.Figure 3 - Evolution of confirmed cases and hot spots, clusters and outliers of incidence rate by municipalityThe Norte region, namely the Porto metropolitan region, recorded a very high rate of incidence in all periods and broke records in the numbers of new cases, except in the third wave, after the Christmas and New Year festivities, in which the number of new cases was the highest ever in every region and specially in Centro region inland municipalities.The results of OLS (Figure 4) are in line with other studies [13, 14] and show that there is a significant relationship in regard to family size that is visible during almost every period, demonstrating that it is difficult to avoid contagion between cohabitants. Population density also appears as important in various moments, although with lower coefficients.Figure 4 – Ordinary Least Squares resultsEmployment concentrations also appear with a strong spatial relationship with the incidences, as well as the socioeconomic conditions that appear to be represented by different variables (beneficiaries of unemployment benefits, social reintegration allowance, declared income, proportion of house-ownership).The importance of mobility in the virus’s propagation is confirmed, both by type of usual mode of transport and commuting time. The interrelation between school students and incidence may also indicate that increased mobility associated with school attendance is relevant for propagation.ConclusionsArcGIS Pro proved to be crucial and an added value for geographical visualization and for the use of spatial statistics methods, essential in providing evidence-based knowledge about the spatial dynamics of COVID-19 in mainland Portugal. The COVID-19 waves demonstrated different spatial behaviours, with different patterns and thus different community drivers. Income, mobility, population density, family size and employment concentrations appear as the most important spatial determinants. Results are in line with scientific literature and prove the relevance of spatial approaches in epidemiology.References1 - WHO - World Health Organization. (2019). Ten threats to global health in 2019. https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-20192 - WHO - World Health Organization. (2020a). Coronavirus disease 2019 (COVID-19): situation report, 94. https://apps.who.int/iris/handle/10665/3318653 - WHO - World Health Organization. (2020e). WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-20204 - Gould, P. (1993). The slow plague : a geography of the AIDS pandemic. Blackwell Publishers. https://books.google.pt/books?id=u3Z9QgAACAAJ&dq5 - Cliff, A. D., Hagget, P., Ord, J. K., & Versey, G. R. (1981). Spatial diffusion : an historical geography of epidemics in an island community. Cambridge University Press Cambridge ; New York. https://books.google.pt/books?id=OIaqxwEACAAJ&dq6 - Arroz, M. E. (1979). Difusão espacial da hepatite infecciosa. Finisterra - Revista Portuguesa de Geografia, LV(14) DOI: https://doi.org/10.18055/Finis22377 - Lyseen, A.K.; Nøhr, C.; Sørensen, E.M.; Gudes, O.; Geraghty, E.M.; Shaw, N.T.; Bivona-Tellez, C. (2014). A review and framework for categorizing current research and development in health related geographical information systems (GIS) studies. Yearb Med. Inform. https://doi.org/10.15265%2FIY-2014-00088 - Pfeiffer, D.; Robinson, T; Stevenson, M.; Stevens, K.; Rogers, D.; Clements, A. (2008). Spatial Analysis in Epidemiology. Oxford University Press. https://books.google.pt/books/about/Spatial_Analysis_in_Epidemiology.html?id=niTDr3SIEhUC&redir_esc=y9 - Franch-Pardo, I.; Napoletano, B.M.; Rosete-Verges, F.; Billa, L. Spatial analysis and GIS in the study of COVID-19. A review. (2020). Sci.10 – DGS – Direção-Geral da Saúde (2020). Relatório de Situação. Lisboa: Ministério da Saúde – Direção-Geral da Saúde. https://covid19.min-saude.pt/relatorio-de-situacao/11 – INE – Instituto Nacional de Estatística (s.d.). Portal do INE. Base de dados. https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_base_dados&contexto=bd&selTab=tab212 – GEP – Gabinete de Estratégia e Planeamento (2018). Carta Social. Ministério do Trabalho, Solidariedade e Segurança Social. www.cartasocial.pt13 – Sousa, P., Costa, N. M., Costa, E. M., Rocha, J., Peixoto, V. R., Fernandes, A. C., Gaspar, R., Duarte-Ramos, F., Abrantes, P., & Leite, A. (2021). COMPRIME - Conhecer mais para intervir melhor: Preliminary mapping of municipal level determinants of covid-19 transmission in Portugal at different moments of the 1st epidemic wave. Portuguese Journal of Public Health. https://doi.org/10.1159/00051433414 – Andersen, L. M.; Harden, S. R.; Sugg, M. M.; Runkle, J. D.; Lundquist, T. E. (2021). Analyzing the spatial determinants of local Covid-19 transmission in the United States. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.142396

These indicators are designed to accompany the SHMI publication. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. There has been a fall in the number of spells for some trusts due to COVID-19 impacting on activity from March 2020 onwards and this appears to be an accurate reflection of hospital activity rather than a case of missing data. Contextual indicators on the number of provider spells which are excluded from the SHMI due to them being related to COVID-19 and on the number of provider spells as a percentage of pre-pandemic activity (January 2019 – December 2019) are produced to support the interpretation of the SHMI. These indicators are being published as experimental statistics. Experimental statistics are official statistics which are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. Notes: 1. There is a shortfall in the number of records for Frimley Health NHS Foundation Trust (trust code RDU) and Tameside and Glossop Integrated Care NHS Foundation Trust (trust code RMP). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 2. A number of trusts are currently engaging in a pilot to submit Same Day Emergency Care (SDEC) data to the Emergency Care Data Set (ECDS), rather than the Admitted Patient Care (APC) dataset. As the SHMI is calculated using APC data, this does have the potential to impact on the SHMI value for these trusts. Trusts with SDEC activity removed from the APC data have generally seen an increase in the SHMI value. This is because the observed number of deaths remains approximately the same as the mortality rate for this cohort is very low; secondly, the expected number of deaths decreases because a large number of spells are removed, all of which would have had a small, non-zero risk of mortality contributing to the expected number of deaths. We are working to better understand the planned changes to the recording of SDEC activity and the potential impact on the SHMI. The trusts affected in this publication are: Barts Health NHS Trust (trust code R1H), Cambridge University Hospitals NHS Foundation Trust (trust code RGT), Croydon Health Services NHS Trust (trust code RJ6), Epsom and St Helier University Hospitals NHS Trust (trust code RVR), Frimley Health NHS Foundation Trust (trust code RDU), Imperial College Healthcare NHS Trust (trust code RYJ), Manchester University NHS Foundation Trust (trust code R0A), Norfolk and Norwich University Hospitals NHS Foundation Trust (trust code RM1), and University Hospitals of Derby and Burton NHS Foundation Trust (trust code RTG). 3. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.

This indicator is designed to accompany the SHMI publication. The SHMI includes all deaths reported of patients who were admitted to non-specialist acute trusts in England and either died while in hospital or within 30 days of discharge. Deaths related to COVID-19 are excluded from the SHMI. A contextual indicator on the percentage of deaths reported in the SHMI which occurred in hospital and the percentage which occurred outside of hospital is produced to support the interpretation of the SHMI. Notes: 1. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. Activity that is being coded as COVID-19, and therefore excluded, is monitored in the contextual indicator 'Percentage of provider spells with COVID-19 coding' which is part of this publication. 2. Please note that there has been a fall in the overall number of spells due to COVID-19 impacting on activity from March 2020 onwards and this appears to be an accurate reflection of hospital activity rather than a case of missing data. Further information is available in the contextual indicator ‘Provider spells compared to the pre-pandemic period’ which is part of this publication. 3. Day cases and regular day attenders are excluded from the SHMI. However, some day cases for University College London Hospitals NHS Foundation Trust (trust code RRV) have been incorrectly classified as ordinary admissions meaning that they have been included in the SHMI. Maidstone and Tunbridge Wells NHS Trust (trust code RWF) has submitted a number of records with a patient classification of ‘day case’ or ‘regular day attender’ and an intended management value of ‘patient to stay in hospital for at least one night’. This mismatch has resulted in the patient classification being updated to ‘ordinary admission’ by the Hospital Episode Statistics (HES) data cleaning rules. This may have resulted in the number of ordinary admissions being overstated. The trust has been contacted to clarify what the correct patient classification is for these records. Values for these trusts should therefore be interpreted with caution. 4. There is a shortfall in the number of records for Cambridge University Hospitals NHS Foundation Trust (trust code RGT) and Northern Care Alliance NHS Foundation Trust (trust code RM3). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 5. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.

These indicators are designed to accompany the SHMI publication. The SHMI methodology does not make any adjustment for patients who are recorded as receiving palliative care. This is because there is considerable variation between trusts in the way that palliative care is recorded. Contextual indicators on the percentage of provider spells and deaths reported in the SHMI where palliative care was recorded at either treatment or specialty level are produced to support the interpretation of the SHMI. Notes: 1. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. Activity that is being coded as COVID-19, and therefore excluded, is monitored in the contextual indicator 'Percentage of provider spells with COVID-19 coding' which is part of this publication. 2. Please note that there has been a fall in the overall number of spells due to COVID-19 impacting on activity from March 2020 onwards and this appears to be an accurate reflection of hospital activity rather than a case of missing data. Further information is available in the contextual indicator ‘Provider spells compared to the pre-pandemic period’ which is part of this publication. 3. Day cases and regular day attenders are excluded from the SHMI. However, some day cases for University College London Hospitals NHS Foundation Trust (trust code RRV) have been incorrectly classified as ordinary admissions meaning that they have been included in the SHMI. Maidstone and Tunbridge Wells NHS Trust (trust code RWF) has submitted a number of records with a patient classification of ‘day case’ or ‘regular day attender’ and an intended management value of ‘patient to stay in hospital for at least one night’. This mismatch has resulted in the patient classification being updated to ‘ordinary admission’ by the Hospital Episode Statistics (HES) data cleaning rules. This may have resulted in the number of ordinary admissions being overstated. The trust has been contacted to clarify what the correct patient classification is for these records. Values for these trusts should therefore be interpreted with caution. 4. There is a shortfall in the number of records for Cambridge University Hospitals NHS Foundation Trust (trust code RGT) and Northern Care Alliance NHS Foundation Trust (trust code RM3). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 5. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.

These indicators are designed to accompany the SHMI publication. The SHMI methodology does not make any adjustment for deprivation. This is because adjusting for deprivation might create the impression that a higher death rate for those who are more deprived is acceptable. Patient records are assigned to 1 of 5 deprivation groups (called quintiles) using the Index of Multiple Deprivation (IMD). The deprivation quintile cannot be calculated for some records e.g. because the patient's postcode is unknown or they are not resident in England. Contextual indicators on the percentage of provider spells and deaths reported in the SHMI belonging to each deprivation quintile are produced to support the interpretation of the SHMI. Notes: 1. As of the July 2020 publication, COVID-19 activity has been excluded from the SHMI. The SHMI is not designed for this type of pandemic activity and the statistical modelling used to calculate the SHMI may not be as robust if such activity were included. Activity that is being coded as COVID-19, and therefore excluded, is monitored in the contextual indicator 'Percentage of provider spells with COVID-19 coding' which is part of this publication. 2. Please note that there has been a fall in the overall number of spells due to COVID-19 impacting on activity from March 2020 onwards and this appears to be an accurate reflection of hospital activity rather than a case of missing data. Further information is available in the contextual indicator ‘Provider spells compared to the pre-pandemic period’ which is part of this publication. 3. Day cases and regular day attenders are excluded from the SHMI. However, some day cases for University College London Hospitals NHS Foundation Trust (trust code RRV) have been incorrectly classified as ordinary admissions meaning that they have been included in the SHMI. Maidstone and Tunbridge Wells NHS Trust (trust code RWF) has submitted a number of records with a patient classification of ‘day case’ or ‘regular day attender’ and an intended management value of ‘patient to stay in hospital for at least one night’. This mismatch has resulted in the patient classification being updated to ‘ordinary admission’ by the HES data cleaning rules. This may have resulted in the number of ordinary admissions being overstated. The trust has been contacted to clarify what the correct patient classification is for these records. Values for these trusts should therefore be interpreted with caution. 4. There is a shortfall in the number of records for Cambridge University Hospitals NHS Foundation Trust (trust code RGT) and Northern Care Alliance NHS Foundation Trust (trust code RM3). Values for these trusts are based on incomplete data and should therefore be interpreted with caution. 5. Further information on data quality can be found in the SHMI background quality report, which can be downloaded from the 'Resources' section of the publication page.