diff options
| author | mike <mike1994vink@gmail.com> | 2021-04-15 13:42:35 +0200 |
|---|---|---|
| committer | mike <mike1994vink@gmail.com> | 2021-04-15 13:42:35 +0200 |
| commit | 24c8870eb5d47f9a8fb78d9fe65a1c41ba72c64d (patch) | |
| tree | a76a1a54ba13887f03f46704b4deae0311e21ad5 /bussiness_understanding | |
| parent | a82fc7e9dc1901c3f342318f14643531d9ad787f (diff) | |
update
Diffstat (limited to 'bussiness_understanding')
| -rw-r--r-- | bussiness_understanding/main.acn | 13 | ||||
| -rw-r--r-- | bussiness_understanding/main.acr | 12 | ||||
| -rw-r--r-- | bussiness_understanding/main.alg | 7 | ||||
| -rw-r--r-- | bussiness_understanding/main.fdb_latexmk | 86 | ||||
| -rw-r--r-- | bussiness_understanding/main.log | 931 | ||||
| -rw-r--r-- | bussiness_understanding/main.run.xml | 86 | ||||
| -rw-r--r-- | bussiness_understanding/main.tex | 732 |
7 files changed, 1430 insertions, 437 deletions
diff --git a/bussiness_understanding/main.acn b/bussiness_understanding/main.acn new file mode 100644 index 0000000..f785fb6 --- /dev/null +++ b/bussiness_understanding/main.acn @@ -0,0 +1,13 @@ +\glossaryentry{HA?\glossentry{ha}|setentrycounter[]{page}\glsnumberformat}{1} +\glossaryentry{NA?\glossentry{na}|setentrycounter[]{page}\glsnumberformat}{1} +\glossaryentry{RNA?\glossentry{rna}|setentrycounter[]{page}\glsnumberformat}{2} +\glossaryentry{RNA?\glossentry{rna}|setentrycounter[]{page}\glsnumberformat}{2} +\glossaryentry{RNA?\glossentry{rna}|setentrycounter[]{page}\glsnumberformat}{2} +\glossaryentry{HA?\glossentry{ha}|setentrycounter[]{page}\glsnumberformat}{2} +\glossaryentry{NA?\glossentry{na}|setentrycounter[]{page}\glsnumberformat}{2} +\glossaryentry{HA?\glossentry{ha}|setentrycounter[]{page}\glsnumberformat}{2} +\glossaryentry{NA?\glossentry{na}|setentrycounter[]{page}\glsnumberformat}{2} +\glossaryentry{HA?\glossentry{ha}|setentrycounter[]{page}\glsnumberformat}{2} +\glossaryentry{NA?\glossentry{na}|setentrycounter[]{page}\glsnumberformat}{2} +\glossaryentry{HA?\glossentry{ha}|setentrycounter[]{page}\glsnumberformat}{2} +\glossaryentry{NA?\glossentry{na}|setentrycounter[]{page}\glsnumberformat}{2} diff --git a/bussiness_understanding/main.acr b/bussiness_understanding/main.acr new file mode 100644 index 0000000..ffcdd2f --- /dev/null +++ b/bussiness_understanding/main.acr @@ -0,0 +1,12 @@ +\glossarysection[\glossarytoctitle]{\glossarytitle}\glossarypreamble +\begin{theglossary}\glossaryheader +\glsgroupheading{H}\relax \glsresetentrylist % +\glossentry{ha}{\glossaryentrynumbers{\relax + \setentrycounter[]{page}\glsnumberformat{1\delimN 2}}}\glsgroupskip +\glsgroupheading{N}\relax \glsresetentrylist % +\glossentry{na}{\glossaryentrynumbers{\relax + \setentrycounter[]{page}\glsnumberformat{1\delimN 2}}}\glsgroupskip +\glsgroupheading{R}\relax \glsresetentrylist % +\glossentry{rna}{\glossaryentrynumbers{\relax + \setentrycounter[]{page}\glsnumberformat{1\delimN 2}}}% +\end{theglossary}\glossarypostamble diff --git a/bussiness_understanding/main.alg b/bussiness_understanding/main.alg new file mode 100644 index 0000000..51b1ba5 --- /dev/null +++ b/bussiness_understanding/main.alg @@ -0,0 +1,7 @@ +This is makeindex, version 2.15 [TeX Live 2021] (kpathsea + Thai support). +Scanning style file ./main.ist.............................done (29 attributes redefined, 0 ignored). +Scanning input file main.acn....done (13 entries accepted, 0 rejected). +Sorting entries....done (55 comparisons). +Generating output file main.acr....done (12 lines written, 0 warnings). +Output written in main.acr. +Transcript written in main.alg. diff --git a/bussiness_understanding/main.fdb_latexmk b/bussiness_understanding/main.fdb_latexmk index 7577e7a..76a8343 100644 --- a/bussiness_understanding/main.fdb_latexmk +++ b/bussiness_understanding/main.fdb_latexmk @@ -1,16 +1,18 @@ # Fdb version 3 -["bibtex main"] 1617901095 "main.aux" "main.bbl" "main" 1617901097 - "../references.bib" 1617885468 112119 54fbd8d5a8b5e56eeee2a739fd592ffb "" - "/usr/local/texlive/2021/texmf-dist/bibtex/bst/base/unsrt.bst" 1292289607 18030 1376b4b231b50c66211e47e42eda2875 "" - "main.aux" 1617901096 2290 3e41b0204a2a747f270fe8349709237e "pdflatex" +["biber main"] 1618220201 "main.bcf" "main.bbl" "main" 1618236262 + "main.bcf" 1618236261 109466 74b22c75bf7e992130e55bf1d06020d4 "pdflatex" (generated) "main.blg" "main.bbl" -["pdflatex"] 1617901095 "/Users/mike/projects/python/fluprint_exploration/bussiness_understanding/main.tex" "main.pdf" "main" 1617901097 - "../bussiness_glossary.tex" 1617900999 145 069781744a6568af2190177874295877 "" - "../data_mining_glossary.tex" 1617901090 94 c0c22536348f25db569abd2952cd8d52 "" - "../preamble.tex" 1617900686 576 a874d20db4ada75cf01df9369f99509f "" - "/Users/mike/projects/python/fluprint_exploration/bussiness_understanding/main.tex" 1617901005 11919 2250872ce6bd4721ea5535444e080bde "" +["pdflatex"] 1618236258 "/Users/mike/projects/python/fluprint_exploration/bussiness_understanding/main.tex" "main.pdf" "main" 1618236262 + "../acronyms.tex" 1618039945 110 f069a231fc0517b6d10593b38498dc7e "" + "../bussiness_glossary.tex" 1618043549 1760 f756a09796bff0586cfc2fe9317428b4 "" + "../data_mining_glossary.tex" 1617901163 93 0cccec44a840131663a83dc2588e75d5 "" + "../images//feature_selection_classification.png" 1618235773 50152 50728e3db6bc69bfd06292673d8acc69 "" + "../images//model_and_vis_plan.png" 1618231687 31674 5cf1eee13183c8cae36e8e5adca45da7 "" + "../images//sql_querying_plan.png" 1618222815 4070 35fe9ffda9abdff4ef27c033958a19f9 "" + "../preamble.tex" 1618236255 1439 d48f76c7e6d870dc36f050f35ef4b6ce "" + "/Users/mike/projects/python/fluprint_exploration/bussiness_understanding/main.tex" 1618236197 16047 c09374954c72fa0ad006f93a00a482dc "" "/usr/local/texlive/2021/texmf-dist/fonts/enc/dvips/cm-super/cm-super-ts1.enc" 1136849721 2900 1537cc8184ad1792082cd229ecc269f4 "" 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b/bussiness_understanding/main.tex index f01b002..6953d29 100644 --- a/bussiness_understanding/main.tex +++ b/bussiness_understanding/main.tex @@ -1,436 +1,338 @@ % hello \input{../preamble.tex} - - \makeglossaries \input{../bussiness_glossary.tex} \input{../data_mining_glossary.tex} +\input{../acronyms.tex} \begin{document} \MyTitle{Bussiness Understanding Report} \tableofcontents \printglossary[type=bus] \printglossary[type=dm] +\printglossary[type=\acronymtype] -\section{Main papers that will be used in this work} -\Gls{latex} is not cool. It never works. +\section{background} -A \gls{model} is a model. +Influenza viruses are enveloped \gls{rnaVirus} (\acrshort{rna} virus(es)) and +are divided into three types on the basis of \gls{antigen}ic differences of internal +structural proteins \citep{fdaGuidanceIndustryClinical2007}. + +Two influenza virus types, Type A and B, cause yearly epidemic outbreaks in humans +and are further classified based on the structure of two major external +\gls{glycoprotein}s, hemagglutinin (\acrshort{ha}) and neuraminidase (\acrshort{na}) +\citep{fdaGuidanceIndustryClinical2007}. + +Type B viruses, which are largely restricted to the human host, have a single +\acrshort{ha} and \acrshort{na} subtype. In contrast, numerous \acrshort{ha} +and \acrshort{na} Type A influenza subtypes have been identified to date. Type +A and B influenza variant strains emerge as a result of frequent +\gls{antigen}ic change, principally from \gls{mutation}s in the \acrshort{ha} +and \acrshort{na} \gls{glycoprotein}s \citep{fdaGuidanceIndustryClinical2007}. + +Since 1977, influenza A virus subtypes H1N1 and H3N2, and influenza B viruses +have been in global circulation in humans. The current U.S. licensed +\gls{tiv} are formulated to prevent influenza illness +caused by these influenza viruses. Because of the frequent emergence of new +influenza variant strains, the \gls{antigen}ic composition of influenza vaccines +needs to be evaluated yearly, and the \gls{tiv} are reformulated almost every +year. + +Currently, even with full production, manufacturing capacity would not produce +enough seasonal influenza vaccine to vaccinate all those for whom the vaccine +is now recommended \citep{fdaGuidanceIndustryClinical2007}. + +\subsection{Influenza mortality estimation models} + +Numerous works apply regression models to describe seasonal population +influenza mortality \citep{zhouHospitalizationsAssociatedInfluenza2012, +greenMortalityAttributableInfluenza2013, iulianoEstimatesGlobalSeasonal2018}. +Reported are varying age-specific influenza burdens during different seasonal +epidemics for different regions, but in general young children an elderly are +found to be more susceptible to influenza and are adviced to vaccinated +annually \citep{zhouHospitalizationsAssociatedInfluenza2012}. + +Specifically, within the US based work of +\cite{zhouHospitalizationsAssociatedInfluenza2012}, the highest hospitalization +rates for influenza were among persons aged $>=$65 years and those aged $<$1 year. +And, age-standardized annual rates per 100000 person-years varied substantially +for influenza. A similar pattern is in +\cite{greenMortalityAttributableInfluenza2013}, where an age shift in Wales and +England seasonal influenza burden was observed following the 2009 swine flue +pandemic. These patterns can confound decision making on national and +international public health policies. The necessity of informed decision making +is apperant from estimates of influenza attributed mortality, it is +estimated that globally 291.243–645.832 influenza associated seasonal deaths +occur annually \citep{iulianoEstimatesGlobalSeasonal2018}. + +\subsection{Vaccine success criteria} +Due to the volume and vulnerability of population groups most at risk for +influenze, the young and the elderly, a placebo controlled vaccine efficacy +study is extremely costly \citep{zhouHospitalizationsAssociatedInfluenza2012}. +Instead the haemagglutination-inhibiting (HAI) antibody test for influenza +virus antibody is used to assess vaccine protection +\citep{dejongHaemagglutinationinhibitingAntibodyInfluenza2003}. The policy for +a succesful vaccine is an 4-fold increase in HAI antibody titre after +vaccination and a geometric mean HAI titer of $\geq$ 40. The last is predicted +to reduce influenza risk by 50\% +\cite{dejongHaemagglutinationinhibitingAntibodyInfluenza2003}. + +\subsection{Finding immunological factors predisposing vaccine HAI antibody response using machine learning} + +It is known that pre-existing T cell populations are correlated with a HAI +antibody response after vaccination. But, the role of T cells in mediating that +response is uncertain. In one work it was found that under certain +circumstances CD8+ T cells specific to conserved viral epitopes correlated with +protection against symptomatic influenza +\citep{sridharCellularImmuneCorrelates2013}.In other work, populations of CD4+ +T cells that associated with protective antibody responses after seasonal +influenza vaccinations were found \citep{bentebibelInductionICOSCXCR3}. +\cite{trieuLongtermMaintenanceInfluenzaSpecific2017} reports a stable CD8+ T +cell response and an increased CD4+ T cell response after vaccination. It was +also reported that repeat vaccinations are an important factor in maintaining +CD4+ T cell population \citep{trieuLongtermMaintenanceInfluenzaSpecific2017}. +How exactly these T cell populations factor into protective influenza immunity +and vaccination reponse is not well understood. + +Machine learning has been applied to clinical datasets to find influenza +protection markers, such as the described T cell populations and titers of +related molecules \citep{furmanApoptosisOtherImmune2013, +sobolevAdjuvantedInfluenzaH1N1Vaccination2016, tsangGlobalAnalysesHuman2014}. +These type of studies suffer from data quality issues, such as: inconsistencies +between findings depending on the epidemic season, only focussing on one type +of biological assay to get data, and a low amount of patients/samples. A +succesful vaccination is also often not well defined. + +\subsection{Bussiness objectives} + +Due to the high volume population that needs vaccines, it is important to study +immune correlates to vaccine response. For example, repeat vaccination might +not be necessary if the response is low, or a different vaccine is desired on a +person to person basis depending on immune correlates. Moreover, identifying +patterns between vaccine response and immune correlates furthers the +understanding of the underlying immunological mechanism of influenza +protection. + +This work uses the FluPrint database, which aims to solve some of the data +quality issues of prior studies using clinical datasets comprised of blood and +serum sample assays. It does so by incorporating eigth clinical studies +conducted between 2007 to 2015 using in total 740 patients, including different +types of assays and normalizing their values, and by providing a binary +classification of high- and low-responder to a vaccine. + +The objectives of this work are to answer: \begin{itemize} - \item the fluprint database paper \cite{tomicFluPRINTDatasetMultidimensional2019} - \item other papers + \item Which datasets in the FluPrint database are most interesting? + \item How do different clinical studies compare? + \item What are the differences in efficacy between vaccination types? + \item What is the effect of repeat vaccination on vaccine response? + \item What immunological factors correlate to a high vaccine response? \end{itemize} -\section{background} - -\cite{GuidanceIndustryClinical2007} -Influenza viruses are enveloped ribonucleic acid viruses belonging to the family of -Orthomyxoviridae and are divided into three distinct types on the basis of antigenic differences -of internal structural proteins (Ref. 2). Two influenza types, Type A and B, are responsible for -yearly epidemic outbreaks of respiratory illness in humans and are further classified based on the -structure of two major external glycoproteins, hemagglutinin (HA) and neuraminidase (NA). -Type B viruses, which are largely restricted to the human host, have a single HA and NA -subtype. In contrast, numerous HA and NA Type A influenza subtypes have been identified to -date. Type A strains infect a wide variety of avian and mammalian species. -Type A and B influenza variant strains emerge as a result of frequent antigenic change, -principally from mutations in the HA and NA glycoproteins. These variant strains may arise -through one of two mechanisms: selective point mutations in the viral genome (Refs. 3 and 4) or -from reassortment between two co-circulating strains (Refs. 5 and 6). -Since 1977, influenza A virus subtypes H1N1 and H3N2, and influenza B viruses have been in -global circulation in humans. The current U.S. licensed inactivated trivalent vaccines are -formulated to prevent influenza illness caused by these influenza viruses. Because of the -frequent emergence of new influenza variant strains, the antigenic composition of influenza -vaccines needs to be evaluated yearly, and the trivalent inactivated influenza vaccines are -reformulated almost every year. The immune response elicited by previous vaccination may not -be protective against new variants. -The Centers for Disease Control and Prevention’s (CDC’s) Advisory Committee on -Immunization Practices (ACIP) has expanded the recommendations for receipt of influenza -vaccination to include an increasing scope of at risk populations, currently including pregnant -women, persons 50 years of age and older, and children 6 to 59 months of age (Refs. 7, 8, and 9). - -Increased demand for influenza vaccines, including that resulting from the broader -recommendations, the withdrawal from the U.S. market by several influenza vaccine -manufacturers, and intermittent decreases in vaccine production due to manufacturing problems -have led to shortages or delays in the availability of influenza vaccine over the past several -seasons. These shortages highlight both the complexity of the production process and the need -to increase the availability of influenza vaccines from multiple manufacturers. Currently, even -with full production, manufacturing capacity would not produce enough seasonal influenza -vaccine to vaccinate all those for whom the vaccine is now recommended. Finally, the -availability of adequate supplies of licensed seasonal inactivated influenza vaccines from -multiple manufacturers will be of value in responding to the emergence of a new pandemic -influenza strain. - -\subsection{Influenza mortality papers} - -\cite{thompsonMortalityAssociatedInfluenza2003} -Context Influenza and respiratory syncytial virus (RSV) cause substantial -morbidity and mortality. Statistical methods used to estimate deaths in the -United States attributable to influenza have not accounted for RSV circulation. - -Objective To develop a statistical model using national mortality and viral -surveillance data to estimate annual influenza- and RSV-associated deaths in -the United States, by age group, virus, and influenza type and subtype. - -Design, Setting, and Population Age-specific Poisson regression models using -national viral surveillance data for the 1976-1977 through 1998-1999 seasons -were used to estimate influenza-associated deaths. Influenza- and -RSV-associated deaths were simultaneously estimated for the 1990-1991 through -1998-1999 seasons. - -Main Outcome Measures Attributable deaths for 3 categories: underlying -pneumonia and influenza, underlying respiratory and circulatory, and all -causes. - -Results Annual estimates of influenza-associated deaths increased -significantly beween the 1976-1977 and 1998-1999 seasons for all 3 death -categories (P<.001 for each category). For the 1990-1991 through 1998-1999 -seasons, the greatest mean numbers of deaths were associated with influenza -A(H3N2) viruses, followed by RSV, influenza B, and influenza A(H1N1). Influenza -viruses and RSV, respectively, were associated with annual means (SD) of 8097 -(3084) and 2707 (196) underlying pneumonia and influenza deaths, 36 155 (11 -055) and 11 321 (668) underlying respiratory and circulatory deaths, and 51 203 -(15 081) and 17 358 (1086) all-cause deaths. For underlying respiratory and -circulatory deaths, 90\% of influenza- and 78\% of RSV-associated deaths occurred -among persons aged 65 years or older. Influenza was associated with more deaths -than RSV in all age groups except for children younger than 1 year. On average, -influenza was associated with 3 times as many deaths as RSV. - -Conclusions Mortality associated with both influenza and RSV circulation -disproportionately affects elderly persons. Influenza deaths have increased -substantially in the last 2 decades, in part because of aging of the -population, underscoring the need for better prevention measures, including -more effective vaccines and vaccination programs for elderly persons. - -Influenza infections result in substantial morbidity and mortality nearly every -year1,2 and estimates of this burden have played a pivotal role in formulating -influenza vaccination policy in the United States.3 However, numbers of deaths -attributable to influenza are difficult to estimate directly because influenza -infections typically are not confirmed virologically or specified on hospital -discharge forms or death certificates. In addition, many influenza-associated -deaths occur from secondary complications when influenza viruses are no longer -detectable.4,5 Nonetheless, wintertime influenza epidemics have been shown to -be associated with increased hospitalizations and mortality for many diagnoses, -including congestive heart failure, chronic obstructive pulmonary disease, -pneumonia, and bacterial superinfections.6-9 - -Respiratory syncytial virus (RSV) epidemics often overlap with influenza -epidemics,8,10 and RSV infections have been associated with substantial -morbidity and mortality in young children and more recently in older -adults.10-14 Like influenza, RSV infections can precipitate both cardiac and -pulmonary complications.15-17 Respiratory syncytial virus infections are rarely -diagnosed in adults, in part because available rapid antigen-detection tests -are insensitive in adults and few tests for RSV are requested for this age -group by medical practitioners.16,18 It is likely that some deaths previously -attributed to influenza are actually associated with RSV infection.13,14,19 - -In this study, we provide age-specific estimates of deaths attributable to -influenza, by virus type and subtype, and to RSV using Poisson regression -models that incorporates national respiratory viral surveillance data. Recent -deliberations of the Advisory Committee on Immunization Practices (ACIP) -regarding influenza vaccination recommendations3 guided our choice of age -groups for these analyses. - -\cite{greenMortalityAttributableInfluenza2013} -Very different influenza seasons have been observed from 2008/09-2011/12 in -England and Wales, with the reported burden varying overall and by age group. -The objective of this study was to estimate the impact of influenza on -all-cause and cause-specific mortality during this period. Age-specific -generalised linear regression models fitted with an identity link were -developed, modelling weekly influenza activity through multiplying clinical -influenza-like illness consultation rates with proportion of samples positive -for influenza A or B. To adjust for confounding factors, a similar activity -indicator was calculated for Respiratory Syncytial Virus. Extreme temperature -and seasonal trend were controlled for. Following a severe influenza season in -2008/09 in 65+yr olds (estimated excess of 13,058 influenza A all-cause -deaths), attributed all-cause mortality was not significant during the 2009 -pandemic in this age group and comparatively low levels of influenza A -mortality were seen in post-pandemic seasons. The age shift of the burden of -seasonal influenza from the elderly to young adults during the pandemic -continued into 2010/11; a comparatively larger impact was seen with the same -circulating A(H1N1)pdm09 strain, with the burden of influenza A all-cause -excess mortality in 15–64 yr olds the largest reported during 2008/09–2011/12 -(436 deaths in 15–44 yr olds and 1,274 in 45–64 yr olds). On average, 76\% of -seasonal influenza A all-age attributable deaths had a cardiovascular or -respiratory cause recorded (average of 5,849 influenza A deaths per season), -with nearly a quarter reported for other causes (average of 1,770 influenza A -deaths per season), highlighting the importance of all-cause as well as -cause-specific estimates. No significant influenza B attributable mortality was -detected by season, cause or age group. This analysis forms part of the -preparatory work to establish a routine mortality monitoring system ahead of -introduction of the UK universal childhood seasonal influenza vaccination -programme in 2013/14. - -\cite{iulianoEstimatesGlobalSeasonal2018} -Background -Estimates of influenza-associated mortality are important for national and -international decision making on public health priorities. Previous estimates -of 250.000 500.000 annual influenza deaths are outdated. We updated the -estimated number of global annual influenza-associated respiratory deaths using -country-specific influenza-associated excess respiratory mortality estimates -from 1999–2015. -Methods -We estimated country-specific influenza-associated respiratory excess mortality -rates (EMR) for 33 countries using time series log-linear regression models -with vital death records and influenza surveillance data. To extrapolate -estimates to countries without data, we divided countries into three analytic -divisions for three age groups (<65 years, 65-74 years, and >=75 years) using -WHO Global Health Estimate (GHE) respiratory infection mortality rates. We -calculated mortality rate ratios (MRR) to account for differences in risk of -influenza death across countries by comparing GHE respiratory infection -mortality rates from countries without EMR estimates with those with estimates. -To calculate death estimates for individual countries within each age-specific -analytic division, we multiplied randomly selected mean annual EMRs by the -country's MRR and population. Global 95\% credible interval (CrI) estimates were -obtained from the posterior distribution of the sum of country-specific -estimates to represent the range of possible influenza-associated deaths in a -season or year. We calculated influenza-associated deaths for children younger -than 5 years for 92 countries with high rates of mortality due to respiratory -infection using the same methods. -Findings -EMR-contributing countries represented 57\% of the global population. The -estimated mean annual influenza-associated respiratory EMR ranged from 0.1 to -6.4 per 100.000 individuals for people younger than 65 years, 2.9 to 44.0 per -100.000 individuals for people aged between 65 and 74 years, and 17.9 to 223.5 -per 100.000 for people older than 75 years. We estimated that 291 243–645 832 -seasonal influenza-associated respiratory deaths (4.0–8.8 per 100.000 -individuals) occur annually. The highest mortality rates were estimated in -sub-Saharan Africa (2.8–16.5 per 100 000 individuals), southeast Asia (3.5-9.2 -per 100.000 individuals), and among people aged 75 years or older (51.3-99.4 -per 100.000 individuals). For 92 countries, we estimated that among children -younger than 5 years, 9243-105 690 influenza-associated respiratory deaths -occur annually. -Interpretation -These global influenza-associated respiratory mortality estimates are higher -than previously reported, suggesting that previous estimates might have -underestimated disease burden. The contribution of non-respiratory causes of -death to global influenza-associated mortality should be investigated. +Since this work is an independent study performed for an assignment, the +success criteria for these objective will be loosely defined as providing a +statistical description or to provide insigth in the questions posed in the +objectives. + +The rationale for these questions and succes criteria are based on the scope +of the 3EC project as part of the Applied data science profile and the data +available. The paper of \cite{tomicFluPRINTDatasetMultidimensional2019} on +which this work is mostly based on provides these questions as interesting +directions for further analysis, but does not directly provide the data +necessary to answer them, only the MySQL database containing a great volume of +data. + +\section{Assess situation} + +\subsection{data sources} + +The only source of data used in the project is provided by +\cite{tomicFluPRINTDatasetMultidimensional2019}. It is a MySQL database for +which the installation is described in the +\href{https://github.com/LogIN-/fluprint}{FluPrint Github Repository}. A +template query is also provided by the authors on the github page belonging to +an unpublished work by the same authors +\href{https://github.com/LogIN-/simon-manuscript}{SIMON Github Repository}. +According to the authors, this data is the most interesting for the bussiness +objective of finding repeat vaccination effects and will be used in this work +too \cite{tomicSIMONAutomatedMachine2019}. The authors give this brief +description of the data: + +\begin{displayquote} +The influenza datasets were obtained from the Stanford Data Miner maintained by + the Human Immune Monitoring Center at Stanford University. This included + total of 177 csv files, which were automatically imported to the MySQL + database to facilitate further analysis. The database, named FluPRINT and + its source code, including the installation tutorial are freely available + here and on project's website. Following database installation, you can + obtain data used in the SIMON publication by following MySQL database + query: +\end{displayquote} + +\begin{lstlisting}[language=sql, caption=Query of initial SIMON data, label={lst:QueryTemplate}] +SELECT donors.id AS donor_id, + donor_visits.age AS age, + donor_visits.vaccine_resp AS outcome, + experimental_data.name_formatted AS data_name, + experimental_data.data AS data +FROM donors + LEFT JOIN donor_visits + ON donors.id = donor_visits.donor_id + AND donor_visits.visit_id = 1 + INNER JOIN experimental_data + ON donor_visits.id = experimental_data.donor_visits_id + AND experimental_data.donor_id = donor_visits.donor_id +WHERE donors.gender IS NOT NULL + AND donor_visits.vaccine_resp IS NOT NULL + AND donor_visits.vaccine = 4 +ORDER BY donors.study_donor_id DESC +\end{lstlisting} + +\subsection{Tools and techniques} + +Installation of the FluPrint database will require an installation on a +unix operating system of \href{https://www.mysql.com/}{MySQL}, +\href{https://www.php.net/manual/en/install.php}{PHP}. More details are at the +\href{https://github.com/LogIN-/fluprint}{FluPrint Github Repository}. + +Database querying was done using the \href{https://neovim.io/}{neovim} toolset, +personal configuration can be found +\href{https://github.com/Vinkage/mike_neovim/tree/feature}{here}. + +Since the work this paper is based on uses the R toolset, it is also used here +\citep{tomicFluPRINTDatasetMultidimensional2019, +tomicSIMONAutomatedMachine2019}. Especially crucial is the +\href{https://cran.r-project.org/web/packages/mulset/index.html}{R package +mulset}, which was made by the authors. This package is used to deal with +missing data between different clinical studies and years, and thus will be +used to generate complete data tables in this paper too. All scripts in this +work were composed using tidyverse packages in combination with modelling +packages. + +\subsection{Requirements of the project} + +Requirements of this work are to show ability in using data science methods. +As such, most of the insights will inevitably be a replication of the work done +by the authors of the FluPrint database \cite{tomicSIMONAutomatedMachine2019}, +but all the scripts and analysis done are original work and are supplied +together with the final deliverable. + +Since the data type used here is a database this makes it more complicated for +an examinator to reproduce all code, especially since installing the database +requires a unix operating system. This is not considered problematic +since the queried tables from the database will be included in the final +deliverable. + +Reporting of the project follows the CRISP-DM methodology, where at each +stage of the project a separate report is written during the analysis work. In +the end the most important information is kept and incorporated in a final +report that is assumed to be graded in conjunction with the code. + +\subsection{Assumptions of the project} + +This work assumes that the focus point of the evaluation lies on the +methodology used, and the ability to apply the basic data science methods +learned in the Applied Data Science profile. The answer to business objectives +is assumed to be subjective, and it is assumed that the methods used and +clarity of insights into the data gained are more important. + +It is also assumed that the FluPrint database and other methods used by the +authors \cite{tomicFluPRINTDatasetMultidimensional2019, +tomicSIMONAutomatedMachine2019} are of high quality, and that this is +appropriate for this work. Out of the scope of this work is investigating +whether the preprocessing done for the data in the database is valid, since we +are not domain experts. A method for querying, cleaning, and generating +complete data tables has been provided by the authors and will also be used in +this work. It is assumed that the SQL and R methods (in particular the mulset R +package) in question are allowed to be used as a starting point in this +assignment. + +\subsection{Constraints of the project} + +This work is an unsupervised assignment, and only personal hardware were +available. This put constraints on dataset size and computational requirements +of analyses. The work was done on a Macbook air (2017) with the OSX big-sur +operating system. This means that unix tools were available and there were no +technical constraints. The filetypes are only csv files generated by the SQL +server. + +\section{Data mining goals} + +All bussiness objectives described involve querying data from the FluPrint +database. The goal of the authors of the FluPrint database was to provide a +unqiue opportunity to study immune correlates of high vaccine responders across +different years and clinical studies. The authors also provide a binary +classification for donors. In this work we first and foremost explore the +database, and lastly we apply feature selection methods and classification +models on the most interesting dataset. + +The bussiness objectives can be translated in data mining terminology like so: +\begin{itemize} + \item Explore and describe SQL queries and corresponding csv tables. + \item Model and visualise the different clinical study populations. + \item Model and visualise the difference between vaccination types. + \item Model and visualise repeat vaccination effects. + \item Apply standard feature selection methods to the most interesting dataset. + \item Fit classification models to the most interesting dataset. +\end{itemize} -\subsection{Vaccine success criteria} -\cite{zhouHospitalizationsAssociatedInfluenza2012} - -Background. Age-specific comparisons of influenza and respiratory syncytial -virus (RSV) hospitalization rates can inform prevention efforts, including -vaccine development plans. Previous US studies have not estimated jointly the -burden of these viruses using similar data sources and over many seasons. - -Methods. We estimated influenza and RSV hospitalizations in 5 age categories -(<1, 1–4, 5–49, 50–64, and >=65 years) with data for 13 states from 1993–1994 -through 2007–2008. For each state and age group, we estimated the contribution -of influenza and RSV to hospitalizations for respiratory and circulatory -disease by using negative binomial regression models that incorporated weekly -influenza and RSV surveillance data as covariates. - -Results. Mean rates of influenza and RSV hospitalizations were 63.5 (95\% -confidence interval [CI], 37.5–237) and 55.3 (95\% CI, 44.4–107) per 100000 -person-years, respectively. The highest hospitalization rates for influenza -were among persons aged >=65 years (309/100000; 95\% CI, 186–1100) and those aged -<1 year (151/100000; 95\% CI, 151–660). For RSV, children aged <1 year had the -highest hospitalization rate (2350/100000; 95\% CI, 2220–2520) followed by those -aged 1–4 years (178/100000; 95\% CI, 155–230). Age-standardized annual rates per -100000 person-years varied substantially for influenza (33–100) but less for -RSV (42–77). - -Conclusions. Overall US hospitalization rates for influenza and RSV are -similar; however, their age-specific burdens differ dramatically. Our estimates -are consistent with those from previous studies focusing either on influenza or -RSV. Our approach provides robust national comparisons of hospitalizations -associated with these 2 viral respiratory pathogens by age group and over time. - -\cite{GuidanceIndustryClinical2007} -something about the effectiveness of vaccines. - -\cite{dejongHaemagglutinationinhibitingAntibodyInfluenza2003} - -The results of the haemagglutination-inhibiting (HI) antibody test for -influenza virus antibody in human sera closely match those produced by virus -neutralization assays and are predictive of protection. On the basis of the -data derived from 12 publications concerning healthy adults, we estimated the -median HI titre protecting 50\% of the vaccinees against the virus concerned at -28. This finding supports the current policy requiring vaccines to induce serum -HI titres of > or = 40 to the vaccine viruses in the majority of the vaccinees. -Unfortunately similar studies are scanty for the elderly, the group most at -risk of influenza. There still remain many unsolved technical problems with the -HI assay and we recommend that these problems be studied and the virus -neutralization test as a predictor of resistance to influenza be assessed. -Although the studies on this issue often give conflicting results, they -generally show that HI antibody responses to influenza vaccination tend to -diminish with increasing age, when health is often compromized. Advanced age in -itself seems not to be an independent factor in this process. However, even in -completely healthy elderly individuals the response to vaccination with an -antigenically new virus may be strongly reduced compared with younger -vaccinees. - -\subsection{antibody response vaccine} -\cite{sridharCellularImmuneCorrelates2013} -The role of T cells in mediating heterosubtypic protection against natural -influenza illness in humans is uncertain. The 2009 H1N1 pandemic (pH1N1) -provided a unique natural experiment to determine whether crossreactive -cellular immunity limits symptomatic illness in antibody-naive individuals. We -followed 342 healthy adults through the UK pandemic waves and correlated the -responses of pre-existing T cells to the pH1N1 virus and conserved core protein -epitopes with clinical outcomes after incident pH1N1 infection. Higher -frequencies of pre-existing T cells to conserved CD8 epitopes were found in -individuals who developed less severe illness, with total symptom score having -the strongest inverse correlation with the frequency of interferon-g (IFN-g)+ -interleukin-2 (IL-2)− CD8+ T cells (r = −0.6, P = 0.004). Within this -functional CD8+IFN-g+IL-2− population, cells with the CD45RA+ chemokine (C-C) -receptor 7 (CCR7)− phenotype inversely correlated with symptom score and had -lung-homing and cytotoxic potential. In the absence of crossreactive -neutralizing antibodies, CD8+ T cells specific to conserved viral epitopes -correlated with crossprotection against symptomatic influenza. This protective -immune correlate could guide universal influenza vaccine development. - -\cite{bentebibelInductionICOSCXCR3} -The role of T cells in mediating heterosubtypic protection against natural -influenza illness in humans is uncertain. The 2009 H1N1 pandemic (pH1N1) -provided a unique natural experiment to determine whether crossreactive -cellular immunity limits symptomatic illness in antibody-naive individuals. We -followed 342 healthy adults through the UK pandemic waves and correlated the -responses of pre-existing T cells to the pH1N1 virus and conserved core protein -epitopes with clinical outcomes after incident pH1N1 infection. Higher -frequencies of pre-existing T cells to conserved CD8 epitopes were found in -individuals who developed less severe illness, with total symptom score having -the strongest inverse correlation with the frequency of interferon-g (IFN-g)+ -interleukin-2 (IL-2)− CD8+ T cells (r = −0.6, P = 0.004). Within this -functional CD8+IFN-g+IL-2− population, cells with the CD45RA+ chemokine (C-C) -receptor 7 (CCR7)− phenotype inversely correlated with symptom score and had -lung-homing and cytotoxic potential. In the absence of crossreactive -neutralizing antibodies, CD8+ T cells specific to conserved viral epitopes -correlated with crossprotection against symptomatic influenza. This protective -immune correlate could guide universal influenza vaccine development. - -\cite{trieuLongtermMaintenanceInfluenzaSpecific2017} -Background. Annual vaccination for healthcare workers and other high-risk -groups is the mainstay of the public health strategy to combat influenza. -Inactivated influenza vaccines confer protection by inducing neutralizing -antibodies efficiently against homologous and closely matched virus strains. In -the absence of neutralizing antibodies, cross-reactive T cells have been shown -to limit disease severity. However, animal studies and a study in -immunocompromised children suggested that repeated vaccination hampers CD8+ T -cells. Yet the impact of repeated annual influenza vaccination on both -cross-reactive CD4+ and CD8+ T cells has not been explored, particularly in -healthy adults. Methods. We assembled a unique cohort of healthcare workers -who received a single AS03-adjuvanted H1N1pdm09 vaccine in 2009 and -subsequently either repeated annual vaccination or no further vaccination -during 2010–2013. Blood samples were collected before the influenza season or -vaccination to assess antibody and T-cell responses. Results. Antibody titers -to H1N1pdm09 persisted above the protective level in both the repeated- and -single-vaccination groups. The interferon γ+ (IFN-γ+) and multifunctional CD4+ -T-cell responses were maintained in the repeated group but declined -significantly in the single-vaccination group. The IFN-γ+CD8+ T cells remained -stable in both groups. Conclusions. This study provides the immunological -evidence base for continuing annual influenza vaccination in adults. - -\subsection{Machine learning usage} - -\cite{furmanApoptosisOtherImmune2013} -Despite the importance of the immune system in many diseases, there are -currently no objective benchmarks of immunological health. In an effort to -identifying such markers, we used influenza vaccination in 30 young (20–30 -years) and 59 older subjects (60 to >89 years) as models for strong and weak -immune responses, respectively, and assayed their serological responses to -influenza strains as well as a wide variety of other parameters, including gene -expression, antibodies to hemagglutinin peptides, serum cytokines, cell subset -phenotypes and in vitro cytokine stimulation. Using machine learning, we -identified nine variables that predict the antibody response with 84\% accuracy. -Two of these variables are involved in apoptosis, which positively associated -with the response to vaccination and was confirmed to be a contributor to -vaccine responsiveness in mice. The identification of these biomarkers provides -new insights into what immune features may be most important for immune health. - -\cite{sobolevAdjuvantedInfluenzaH1N1Vaccination2016} -Adjuvanted vaccines afford invaluable protection against disease, and the -molecular and cellular changes they induce offer direct insight into human -immunobiology. Here we show that within 24 h of receiving adjuvanted swine flu -vaccine, healthy individuals made expansive, complex molecular and cellular -responses that included overt lymphoid as well as myeloid contributions. -Unexpectedly, this early response was subtly but significantly different in -people older than ~35 years. Wide-ranging adverse clinical events can seriously -confound vaccine adoption, but whether there are immunological correlates of -these is unknown. Here we identify a molecular signature of adverse events -that was commonly associated with an existing B cell phenotype. Thus -immunophenotypic variation among healthy humans may be manifest in complex -pathophysiological responses. - -\cite{tsangGlobalAnalysesHuman2014} -A major goal of systems biology is the development of models that accurately -predict responses to perturbation. Constructing such models requires the -collection of dense measurements of system states, yet transformation of data -into predictive constructs remains a challenge. To begin to model human -immunity, we analyzed immune parameters in depth both at baseline and in -response to influenza vaccination. Peripheral blood mononuclear cell -transcriptomes, serum titers, cell subpopulation frequencies, and B cell -responses were assessed in 63 individuals before and after vaccination and were -used to develop a systematic framework to dissect inter- and intra-individual -variation and build predictive models of postvaccination antibody responses. -Strikingly, independent of age and pre-existing antibody titers, accurate -models could be constructed using pre-perturbation cell populations alone, -which were validated using independent baseline time points. Most of the -parameters contributing to prediction delineated temporally stable baseline -differences across individuals, raising the prospect of immune monitoring -before intervention. - -\subsection{Problems of previous studies} -\cite{chattopadhyaySinglecellTechnologiesMonitoring2014} -The complex heterogeneity of cells, and their interconnectedness with each -other, are major challenges to identifying clinically relevant measurements -that reflect the state and capability of the immune system. Highly multiplexed, -single-cell technologies may be critical for identifying correlates of disease -or immunological interventions as well as for elucidating the underlying -mechanisms of immunity. Here we review limitations of bulk measurements and -explore advances in single-cell technologies that overcome these problems by -expanding the depth and breadth of functional and phenotypic analysis in space -and time. The geometric increases in complexity of data make formidable hurdles -for exploring, analyzing and presenting results. We summarize recent approaches -to making such computations tractable and discuss challenges for integrating -heterogeneous data obtained using these single-cell technologies. - -\cite{galliEndOmicsHigh2019} -High-dimensional single-cell (HDcyto) technologies, such as mass cytometry -(CyTOF) and flow cytometry, are the key techniques that hold a great promise -for deciphering complex biological processes. During the last decade, we -witnessed an exponential increase of novel HDcyto technologies that are able to -deliver an in-depth profiling in different settings, such as various autoimmune -diseases and cancer. The concurrent advance of custom data-mining algorithms -has provided a rich substrate for the development of novel tools in -translational medicine research. HDcyto technologies have been successfully -used to investigate cellular cues driving pathophysiological conditions, and to -identify disease-specific signatures that may serve as diagnostic biomarkers or -therapeutic targets. These technologies now also offer the possibility to -describe a complete cellular environment, providing unanticipated insights into -human biology. In this review, we present an update on the current cutting-edge -HDcyto technologies and their applications, which are going to be fundamental -in providing further insights into human immunology and pathophysiology of -various diseases. Importantly, we further provide an overview of the main -algorithms currently available for data mining, together with the conceptual -workflow for high-dimensional cytometric data handling and analysis. Overall, -this review aims to be a handy overview for immunologists on how to design, -develop and read HDcyto data. - -\cite{simoniMassCytometryPowerful2018} -Advancement in methodologies for single cell analysis has historically been a -major driver of progress in immunology. Currently, high dimensional flow -cytometry, mass cytometry and various forms of single cell sequencing-based -analysis methods are being widely adopted to expose the staggering -heterogeneity of immune cells in many contexts. Here, we focus on mass -cytometry, a form of flow cytometry that allows for simultaneous interrogation -of more than 40 different marker molecules, including cytokines and -transcription factors, without the need for spectral compensation. We argue -that mass cytometry occupies an important niche within the landscape of -single-cell analysis platforms that enables the efficient and in-depth study of -diverse immune cell subsets with an ability to zoom-in on myeloid and lymphoid -compartments in various tissues in health and disease. We further discuss the -unique features of mass cytometry that are favorable for combining multiplex -peptide-MHC multimer technology and phenotypic characterization of antigen -specific T cells. By referring to recent studies revealing the complexities of -tumor immune infiltrates, we highlight the particular importance of this -technology for studying cancer in the context of cancer immunotherapy. Finally, -we provide thoughts on current technical limitations and how we imagine these -being overcome. - -\bibliographystyle{unsrt} -\bibliography{../references.bib} +In data mining terms, the problem type is a combination of exploratory data +analysis and classification. Since this work is for a 3EC assignment for the +Applied Data Science profile and most of the goals are exploratory analyses, +success criteria for all goals are subjective. For exploratory and visual type +goals the quality is expected to be of the same level as the publications of +the authors \cite{tomicFluPRINTDatasetMultidimensional2019, +tomicSIMONAutomatedMachine2019}. For the classification type goals we follow +the model evaluation procedure used by the authors +\cite{tomicSIMONAutomatedMachine2019}, models were evaluated by the AUROC +metric, and accuracy, specificity and sensitivity were also reported. Insights +produced by this work were benchmarked against the work of the original +authors. + +\section{Project plan} + +\f{sql_querying_plan} +{Project plan for the SQL related data mining goal.} +{plan:sql} + +The first part of the project involved querying the database, and collecting +and describing the available data \autoref{plan:sql}. The first goal is to +understand the tables in the SQL database, their key relations, and to describe +the attributes within the tables. Valuable info on this part is already +provided in the original publication of the database +\cite{tomicFluPRINTDatasetMultidimensional2019}, but it was also investigated +in this work. The tools that will be used are SQL for querying and R for +statistical descriptions. + +The second phase of this plan was an iterative process of finding suitable data +to answer the modelling and visualisation data mining goals. This is a more +involved process since it requires exploration of the database to answer the +questions, and therefore was estimated to take time. + +\f{model_and_vis_plan} +{Project plan for the modelling and visualisation data mining goals.} +{plan:vis} + +Relations between attributes in the generated datasets are visualised and +modelled to see if there exist a pattern in the data that is relevant for the +business objectives \autoref{plan:vis}. A critical point in this plan is +deciding whether an objective cannot be answered with the available data. In +that case the goal was revised and the second phase of the SQL query plan was +reiterated. When deciding if the exploratory analysis was of sufficient +quality, the work by the authors of the database used in this work was used as +a subjective benchmark \cite{tomicSIMONAutomatedMachine2019, +tomicFluPRINTDatasetMultidimensional2019}. + +\f{feature_selection_classification} +{Project plan for the classification and feature selection data mining goal.} +{plan:cls} + +For the final two data mining goals the plan was to find the immune correlates +of high immune responders using a wrapper based feature selection strategy +\autoref{plan:cls} + +\printbibliography \end{document} |