Temporal Analysis of the Influence of the Number of Vaccinations on the Number of Covid-19 Cases in South Sulawesi Province Using ARIMAX Model

South Sulawesi is a province that has the highest number of Covid-19 cases in the Sulawesi island region. Vaccination is one way that is considered effective in controlling the infection of a disease. Covid-19 vaccination in Indonesia was carried out in January 2021. This study aims to obtain the best Autoregressive Integrated Moving Average X (ARIMAX) model in modeling the effect of the number of vaccinations on the number of Covid-19 cases in South Sulawesi Province. Data on the number of vaccinations and Covid-19 cases in South Sulawesi Province (October 1, 2021 - January 31, 2022) were used. The best ARIMAX model in modeling Covid-19 in relation to the number of vaccinations is ARIMAX (2,1,0). The results showed that the number of vaccinations had a negative effect on the number of Covid-19 cases at the significant level of 10%. This indicates that if the number of vaccinations increases then the number of Covid-19 cases will decrease

Spatial epidemiological approaches to inform leptospirosis surveillance and control: a systematic review and critical appraisal of methods

Leptospirosis is a global zoonotic disease that the transmission is driven by complex geographical and temporal variation in demographics, animal hosts and socioecological factors. This results in complex challenges for the identification of high‐risk areas. Spatial and temporal epidemiological tools could be used to support leptospirosis control programs, but the adequacy of its application has not been evaluated. We searched literature in six databases including PubMed, Web of Science, EMBASE, Scopus, SciELO and Zoological Record to systematically review and critically assess the use of spatial and temporal analytical tools for leptospirosis and to provide general framework for its application in future studies. We reviewed 115 articles published between 1930 and October 2018 from 41 different countries. Of these, 65 (56.52%) articles were on human leptospirosis, 39 (33.91%) on animal leptospirosis and 11 (9.5%) used data from both human and animal leptospirosis. Spatial analytical (n = 106) tools were used to describe the distribution of incidence/prevalence at various geographical scales (96.5%) and to explored spatial patterns to detect clustering and hot spots (33%). A total of 51 studies modelled the relationships of various variables on the risk of human (n = 31), animal (n = 17) and both human and animal infection (n = 3). Among those modelling studies, few studies had generated spatially structured models and predictive maps of human (n = 2/31) and animal leptospirosis (n = 1/17). In addition, nine studies applied time‐series analytical tools to predict leptospirosis incidence. Spatial and temporal analytical tools have been greatly utilized to improve our understanding on leptospirosis epidemiology. Yet the quality of the epidemiological data, the selection of covariates and spatial analytical techniques should be carefully considered in future studies to improve usefulness of evidence as tools to support leptospirosis control. A general framework for the application of spatial analytical tools for leptospirosis was proposed

Comparing social media and Google to detect and predict severe epidemics

Internet technologies have demonstrated their value for the early detection and prediction of epidemics. In diverse cases, electronic surveillance systems can be created by obtaining and analyzing on-line data, complementing other existing monitoring resources. This paper reports the feasibility of building such a system with search engine and social network data. Concretely, this study aims at gathering evidence on which kind of data source leads to better results. Data have been acquired from the Internet by means of a system which gathered real-time data for 23 weeks. Data on infuenza in Greece have been collected from Google and Twitter and they have been compared to infuenza data from the ofcial authority of Europe. The data were analyzed by using two models: the ARIMA model computed estimations based on weekly sums and a customized approximate model which uses daily sums. Results indicate that infuenza was successfully monitored during the test period. Google data show a high Pearson correlation and a relatively low Mean Absolute Percentage Error (R=0.933, MAPE=21.358). Twitter results are slightly better (R=0.943, MAPE=18.742). The alternative model is slightly worse than the ARIMA(X) (R=0.863, MAPE=22.614), but with a higher mean deviation (abs. mean dev: 5.99% vs 4.74%)

Analisis temporal efek cuaca terhadap leptospirosis di kabupaten Bantul, Yogyakarta tahun 2010-2018

Tujuan: Menganalisis efek suhu udara, kelembapan udara dan curah hujan terhadap kejadian leptospirosis secara temporal di Kabupaten Bantul tahun 2010-2018. Metode: Desain penelitian menggunakan studi ekologi berbasis time-series, antara faktor cuaca (suhu udara, kelembapan udara dan curah hujan) dari stasiun cuaca Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) DIY dan kejadian bulanan leptospirosis di Kabupaten Bantul selama periode 9 tahun, 2010-2018. Pearson’s correlation dan time-lag correlation dilakukan dengan STATA 13 guna mengamati asosiasi secara temporal, selanjutnya disajikan dalam grafik time-series dengan Microsoft Excel. Hasil: Karakteristik cuaca di Kabupaten Bantul untuk suhu udara, kelembapan udara, dan curah hujan masing-masing sebesar 27.2°C, 84%, dan 171 mm. Kejadian leptospirosis selama 2010-2018 sejumlah 779 kasus, tertinggi 120 kasus di bulan Mei dan 154 kasus pada tahun 2011. Suhu udara 3 bulan sebelumya (lag 3) berhubungan positif dan lemah terhadap kejadian leptospirosis (r=0.2493). Pola fluktuasi grafik time-series suhu udara tidak diikuti kejadian leptospirosis pada 2 tahun awal dan akhir periode. Kelembapan udara 1 bulan sebelumya (lag 1) berhubungan positif dan lemah terhadap kejadian leptospirosis (r=0.2921). Pola fluktuasi grafik time-series kelembapan udara tidak diikuti kejadian leptospirosis pada 2 tahun awal periode. Curah hujan 3 bulan sebelumya (lag 3) berhubungan positif dan sedang terhadap kejadian leptospirosis (r=0.5297). Pola fluktuasi grafik time-series curah hujan diikuti kejadian leptospirosis selama periode. Simpulan: Kejadian leptospirosis berhubungan dengan efek time-lag suhu udara, kelembapan udara dan curah hujan yang terjadi beberapa bulan sebelumnya. Diperlukan sistem kewaspadaan dini pemerintah dan masyarakat di daerah endemis menghadapi ancaman leptospirosis selama musim hujan

Modelling longitudinal data on respiratory infections to inform health policy

Detecting the start of an outbreak, quantifying its burden, disentangling the contribution of different pathogens and evaluating the effectiveness of an intervention are research questions common to several infectious diseases. The answers to these questions provide the epidemiological understanding to prevent future outbreaks, by informing public health policies such as drug stockpiling, vaccination regimes or non-medical interventions. We investigate the use of statistical models to quantify burden of respiratory disease and evaluate effectiveness of public health interventions, while accounting for the challenges posed by surveillance data. The observational nature of the available information, affected by confounding, makes causal statements difficult. Improvements to routinely employed methodologies are proposed, employing phenomenological models to estimate a counterfactual, i.e. what what would have happened in the absence of a contributing factor or intervention. We apply these methods to different types of studies, to address specific gaps in the literature. S. pneumoniae is the leading cause of respiratory morbidity and mortality globally, especially in young children and in the elderly. To improve the understanding of factors triggering disease progression, we firstly analyse individual-level information about pneumococcal carriage and lower respiratory tract infection with a multi-state model, using data from a cohort study in Thailand. Secondly, we clarify the role of viral coinfection and meteorological conditions in invasive pneumococcal disease (IPD) incidence using English surveillance data. A novel multivariate linear regression model is proposed to estimate the influenza-specific contribution additional to the seasonal IPD burden across age groups. We then quantify the impact of the currently implemented vaccination policy, by estimating the counterfactual of IPD incidence in absence of vaccination. This allows disentangling serotype replacement from the vaccine effect, making use of a synthetic control approach. Finally, an empirical dynamical modelling strategy is employed to quantify the interaction between influenza and pneumococcus. Counterfactual analysis can also be employed to quantify the burden of novel respiratory pathogens. The last application of this approach is to estimate the excess mortality during the the COVID-19 pandemic in England

Modeling Precipitation, Acute Gastrointestinal Illness, and Environmental Factors in North Carolina, USA

Increasing intensity and frequency of extreme weather events due to climate change underscores the importance of understanding the influence of hydroclimatic variability on health. Meteorological drivers affect rates of acute gastrointestinal illness (AGI), but the association between precipitation and AGI, the sensitivity to modeling decisions, and the effects of sociodemographic and environmental risk factors are not well characterized. Furthermore, methodological differences may reduce inter-study comparability and can affect model estimates.In this dissertation, we reviewed the methodologies of recent time series AGI-weather studies, including outcome and exposure variables, data sources, spatiotemporal aggregation, and model specification. To investigate the sensitivity of the association between AGI and precipitation to exposure definitions and effect measure modification (EMM), we used AGI emergency department (ED) visit and weather data (2008-2015) from North Carolina (NC) to develop daily, ZIP code-level quasi-Poisson generalized linear models and distributed lag models. We compared multiple precipitation metrics: absolute (total precipitation), extreme (90th, 95th, and 99th percentiles with and without zero-precipitation days), and antecedent (cumulative wet-dry days; 8-week wet-dry periods). We assessed for potential EMM by physiographic region, the density of hogs in concentrated animal feeding operations (CAFOs), and percent of population on private drinking water wells.Depending on exposure definition, we observed an overall cumulative decrease of 1-18% in AGI ED rates following extreme precipitation events (over 0-7 days), with stronger effects associated with heavier rainfall, and a 2% (95% CI: 1.02, 1.03) increase after antecedent (8-week) wet periods. Inverse statewide results following extreme precipitation—dominated by the demographic weight of urban centers in the Piedmont region—were consistent with dilution effects posited by the concentration-dilution hypothesis but obscured dramatic sub-state variation. While EMM by private wells was inconclusive, region and hog density strongly modified the associations observed, with increased AGI ED rates following 95th percentile precipitation in the mountains (18%), coastal plains (19%), and areas exposed to hog CAFOs (7-15%). Our results reveal the vulnerability of mountainous, coastal, and CAFO-impacted areas in NC to rainfall-exacerbated AGI risk. This dissertation highlights the hazards of data aggregation and importance of precipitation exposure definitions and effect measure modification when modeling climate-health relationships.Doctor of Philosoph

Dengue Fever in Ribeirão Preto, Brazil, 2003-2012: Patterns of Disease and Understanding of Prevention.

Dengue fever is the most important arboviral disease of modernity. It is caused by four serologically distinct viruses transmitted by Aedes mosquitoes. Over 2.3 million cases were reported in the Americas during 2013. More than half of the world’s population is at risk of infection and the vectors’ distributions are increasing. Current prevention strategies involve personal protection and disruption of vector breeding sites as no vaccine is available. Accordingly, it is important to understand the underlying risk factors of dengue prevention if transmission risk is to be reduced. To assess underlying risk factors, three studies were conducted using data from Ribeirao Preto, Brazil. The first characterized epidemiologic patterns of dengue surveillance data that included all reported cases in the city during 2003-2012. The second study employed a negative binomial GLM to assess the potential links between weather and dengue. Finally, a large survey was conducted in 2012 aimed at characterizing population-level knowledge of dengue. Annual dengue incidence varied considerably from <1 to almost 500 cases per 10,000 inhabitants. Incidence did not vary by sex, but was inversely associated with educational levels. Analyses suggested that 1/3 of the city population has been infected with dengue during the study period. Results from the weather study indicated that increases in minimum temperature and precipitation were associated with dengue cases lagged by 6 and 8 weeks, respectively. Results from the survey indicated that residents had general knowledge about various aspects of dengue, but that individuals with lower income and SES had lower levels of knowledge. Taken together, these three studies provide additional information that may explain the drivers of dengue transmission. It appears that many residents may be primed for more severe disease in the near future. Also, upon validation, the dengue-weather model may help officials predict and better prepare for future outbreaks. Finally, survey findings indicated a need for targeted campaigns to improve dengue knowledge amongst those at highest risk of disease. This combination of these epidemiological approaches provides a low-cost basis for assessment of multiple aspects of dengue etiology, which may be of particular interest in resource-limited locations.PhDEpidemiological ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/110465/1/amarkon_1.pd