Release 0.11.20

Merge pull request #1588 from AMICI-dev/release_0_11_20

Author: dweindl

.github/workflows/test_benchmark_collection_models.yml

@@ -10,6 +10,8 @@ on:
       - master
       - develop
   workflow_dispatch:
+  schedule:
+    - cron:  '48 4 * * *'
 
 jobs:
   build:

.github/workflows/test_doc.yml

@@ -9,6 +9,8 @@ on:
       - develop
       - master
   workflow_dispatch:
+  schedule:
+    - cron:  '48 4 * * *'
 
 jobs:
   doxygen:

.github/workflows/test_matlab.yml

@@ -14,8 +14,8 @@ jobs:
     - run: echo "AMICI_DIR=$(pwd)" >> $GITHUB_ENV
 
     - name: Install MATLAB
-      uses: matlab-actions/setup-matlab@v0
+      uses: matlab-actions/setup-matlab@v1
     - name: Run script
-      uses: matlab-actions/run-command@v0
+      uses: matlab-actions/run-command@v1
       with:
         command: cd matlab; installAMICI; addpath tests; testModels

.github/workflows/test_performance.yml

@@ -12,6 +12,9 @@ on:
 
   workflow_dispatch:
 
+  schedule:
+    - cron:  '48 4 * * *'
+
 jobs:
   build:
     name: Performance Test

.github/workflows/test_python_cplusplus.yml

@@ -20,7 +20,7 @@ jobs:
     - run: echo "BNGPATH=${GITHUB_WORKSPACE}/ThirdParty/BioNetGen-2.7.0" >> $GITHUB_ENV
 
     # sonar cloud
-    - run: echo "SONAR_SCANNER_VERSION=4.5.0.2216" >> $GITHUB_ENV
+    - run: echo "SONAR_SCANNER_VERSION=4.6.1.2450" >> $GITHUB_ENV
     - run: echo "SONAR_SCANNER_HOME=${HOME}/.sonar/sonar-scanner-$SONAR_SCANNER_VERSION-linux" >> $GITHUB_ENV
     - run: echo "SONAR_SCANNER_OPTS=-server" >> $GITHUB_ENV
     - run: echo "${SONAR_SCANNER_HOME}/bin" >> $GITHUB_PATH

.github/workflows/test_python_ver_matrix.yml

@@ -22,7 +22,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: [3.7, 3.8, 3.9]
+        python-version: [3.7, 3.8, 3.9, '3.10']
         experimental: [false]
 # Temporarily disabled because the respective jobs somehow runs infinitely
 #        include:

.github/workflows/test_valgrind.yml

@@ -3,7 +3,7 @@ on:
   push:
     branches:
       - master
-      - feature_cpp_hierarchical_derivatives
+      - update_valgrind_supp
   pull_request:
     branches:
       - master

.readthedocs.yml

@@ -11,19 +11,18 @@ sphinx:
   configuration: documentation/conf.py
   fail_on_warning: True
 
-# Build documentation with MkDocs
-#mkdocs:
-#  configuration: mkdocs.yml
-
-# Optionally build your docs in additional formats such as PDF and ePub
 formats:
   - pdf
 
-# Optionally set the version of Python and requirements required to build your docs
 python:
-  version: 3.8
   install:
     - requirements: documentation/rtd_requirements.txt
 
 build:
-  image: latest
+  os: "ubuntu-20.04"
+  apt_packages:
+    # for custom doxygen
+    - libclang-cpp9
+    - libclang1-9
+  tools:
+    python: "3.8"

CHANGELOG.md

@@ -2,6 +2,23 @@
 
 ## v0.X Series
 
+### v0.11.20 (2021-11-12)
+
+New: 
+ * Changed parameter mappings such that unassigned values have non-nan default values. This fixes erroneous evaluation of `llh` as `NaN` in some situations (#1574)
+ * Added support for Python 3.10 (#1555)
+
+Fixes:
+ * Fixed a bug when simulation start time was not transferred when copying a solver instance (#1573)
+ * Fixed a bug which led to incorrect sensitivies for models with multiple assignment rules or rate rules (#1584)
+
+Other:
+ * Update CI and documentation settings (#1569, #1527, #1572, #1575, #1579, #1580, #1589, #1581)
+ * Extend set of validated benchmark models that is checked during CI (#1571, #1577)
+ * Fixed string formatting in derivative checks (#1585)
+ * Added helper methods to save and restore model instance-only settings (#1576)
+
+
 ### v0.11.19 (2021-10-13)
 
 New:

documentation/amici_refs.bib

@@ -946,6 +946,43 @@ @phdthesis{Maier2021
   doi       = {10.25932/publishup-51587},
   year        = {2021},
 }
+@article {Contento2021.10.01.21263052,
+	author = {Contento, Lorenzo and Castelletti, Noemi and Raim{\'u}ndez, Elba and Le Gleut, Ronan and Sch{\"a}lte, Yannik and Stapor, Paul and Hinske, Ludwig Christian and H{\"o}lscher, Michael and Wieser, Andreas and Radon, Katja and Fuchs, Christiane and Hasenauer, Jan and the KoCo19 study group},
+	title = {Integrative modelling of reported case numbers and seroprevalence reveals time-dependent test efficiency and infection rates},
+	elocation-id = {2021.10.01.21263052},
+	year = {2021},
+	doi = {10.1101/2021.10.01.21263052},
+	publisher = {Cold Spring Harbor Laboratory Press},
+	abstract = {Mathematical models have been widely used during the ongoing SARS-CoV-2 pandemic for data interpretation, forecasting, and policy making. However, most models are based on officially reported case numbers, which depend on test availability and test strategies. The time dependence of these factors renders interpretation difficult and might even result in estimation biases.Here, we present a computational modelling framework that allows for the integration of reported case numbers with seroprevalence estimates obtained from representative population cohorts. To account for the time dependence of infection and testing rates, we embed flexible splines in an epidemiological model. The parameters of these splines are estimated, along with the other parameters, from the available data using a Bayesian approach.The application of this approach to the official case numbers reported for Munich (Germany) and the seroprevalence reported by the prospective COVID-19 Cohort Munich (KoCo19) provides first estimates for the time dependence of the under-reporting factor. Furthermore, we estimate how the effectiveness of non-pharmaceutical interventions and of the testing strategy evolves over time. Overall, our results show that the integration of temporally highly resolved and representative data is beneficial for accurate epidemiological analyses.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis study was funded by the Bavarian State Ministry of Science and the Arts, the University Hospital of Ludwig-Maximilians-University Munich, the Helmholtz Centre Munich, the University of Bonn (via the Transdiciplinary Research Areas), the University of Bielefeld, Munich Center of Health (McHealth) and the German Ministry for Education and Research (MoKoCo19, reference number 01KI20271), German Research Foundation (SEPAN, reference number HA7376/3-1), Volkswagen Stiftung (reference number: 99 450). This work was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany{\textquoteright}s Excellence Strategy EXC 2047/1 - 390685813 and EXC 2151 - 390873048. The ORCHESTRA project has received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under grant agreement No 101016167. The views expressed in this paper are the sole responsibility of the authors and the Commission is not responsible for any use that may be made of the information it contains. The funders had no role in study design, data collection, data analyses, data interpretation, writing, or submission of this manuscript.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The study protocol was approved by the Institutional Review Board at the Ludwig-Maximilians-University in Munich, Germany (opinion date 31 March 2020, number 20-275, opinion date amendment: 10 October 2020), prior to study initiation.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe data used in this manuscript (except data from public sources such as the Robert Koch Institute) cannot be made public due to patient consent.},
+	URL = {https://www.medrxiv.org/content/early/2021/10/01/2021.10.01.21263052},
+	eprint = {https://www.medrxiv.org/content/early/2021/10/01/2021.10.01.21263052.full.pdf},
+	journal = {medRxiv}
+}
+
+@Article{GudinaAli2021,
+  author   = {Esayas Kebede Gudina and Solomon Ali and Eyob Girma and Addisu Gize and Birhanemeskel Tegene and Gadissa Bedada Hundie and Wondewosen Tsegaye Sime and Rozina Ambachew and Alganesh Gebreyohanns and Mahteme Bekele and Abhishek Bakuli and Kira Elsbernd and Simon Merkt and Lorenzo Contento and Michael Hoelscher and Jan Hasenauer and Andreas Wieser and Arne Kroidl},
+  journal  = {The Lancet Global Health},
+  title    = {{Seroepidemiology and model-based prediction of SARS-CoV-2 in Ethiopia: longitudinal cohort study among front-line hospital workers and communities}},
+  year     = {2021},
+  issn     = {2214-109X},
+  number   = {11},
+  pages    = {e1517-e1527},
+  volume   = {9},
+  abstract = {Summary
+Background
+Over 1 year since the first reported case, the true COVID-19 burden in Ethiopia remains unknown due to insufficient surveillance. We aimed to investigate the seroepidemiology of SARS-CoV-2 among front-line hospital workers and communities in Ethiopia.
+Methods
+We did a population-based, longitudinal cohort study at two tertiary teaching hospitals involving hospital workers, rural residents, and urban communities in Jimma and Addis Ababa. Hospital workers were recruited at both hospitals, and community participants were recruited by convenience sampling including urban metropolitan settings, urban and semi-urban settings, and rural communities. Participants were eligible if they were aged 18 years or older, had provided written informed consent, and were willing to provide blood samples by venepuncture. Only one participant per household was recruited. Serology was done with Elecsys anti-SARS-CoV-2 anti-nucleocapsid assay in three consecutive rounds, with a mean interval of 6 weeks between tests, to obtain seroprevalence and incidence estimates within the cohorts.
+Findings
+Between Aug 5, 2020, and April 10, 2021, we did three survey rounds with a total of 1104 hospital workers and 1229 community residents participating. SARS-CoV-2 seroprevalence among hospital workers increased strongly during the study period: in Addis Ababa, it increased from 10·9% (95% credible interval [CrI] 8·3–13·8) in August, 2020, to 53·7% (44·8–62·5) in February, 2021, with an incidence rate of 2223 per 100 000 person-weeks (95% CI 1785–2696); in Jimma Town, it increased from 30·8% (95% CrI 26·9–34·8) in November, 2020, to 56·1% (51·1–61·1) in February, 2021, with an incidence rate of 3810 per 100 000 person-weeks (95% CI 3149–4540). Among urban communities, an almost 40% increase in seroprevalence was observed in early 2021, with incidence rates of 1622 per 100 000 person-weeks (1004–2429) in Jimma Town and 4646 per 100 000 person-weeks (2797–7255) in Addis Ababa. Seroprevalence in rural communities increased from 18·0% (95% CrI 13·5–23·2) in November, 2020, to 31·0% (22·3–40·3) in March, 2021.
+Interpretation
+SARS-CoV-2 spread in Ethiopia has been highly dynamic among hospital worker and urban communities. We can speculate that the greatest wave of SARS-CoV-2 infections is currently evolving in rural Ethiopia, and thus requires focused attention regarding health-care burden and disease prevention.
+Funding
+Bavarian State Ministry of Sciences, Research, and the Arts; Germany Ministry of Education and Research; EU Horizon 2020 programme; Deutsche Forschungsgemeinschaft; and Volkswagenstiftung.},
+  doi      = {https://doi.org/10.1016/S2214-109X(21)00386-7},
+  url      = {https://www.sciencedirect.com/science/article/pii/S2214109X21003867},
+}
+
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