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Mapping 123 million neonatal, infant and child deaths between 2000 and 2017.

March 28, 2020
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Mapping 123 million neonatal, infant and child deaths between 2000 and 2017.

Nature. 2019 10;574(7778):353-358

Authors: Burstein R, Henry NJ, Collison ML, Marczak LB, Sligar A, Watson S, Marquez N, Abbasalizad-Farhangi M, Abbasi M, Abd-Allah F, Abdoli A, Abdollahi M, Abdollahpour I, Abdulkader RS, Abrigo MRM, Acharya D, Adebayo OM, Adekanmbi V, Adham D, Afshari M, Aghaali M, Ahmadi K, Ahmadi M, Ahmadpour E, Ahmed R, Akal CG, Akinyemi JO, Alahdab F, Alam N, Alamene GM, Alene KA, Alijanzadeh M, Alinia C, Alipour V, Aljunid SM, Almalki MJ, Al-Mekhlafi HM, Altirkawi K, Alvis-Guzman N, Amegah AK, Amini S, Amit AML, Anbari Z, Androudi S, Anjomshoa M, Ansari F, Antonio CAT, Arabloo J, Arefi Z, Aremu O, Armoon B, Arora A, Artaman A, Asadi A, Asadi-Aliabadi M, Ashraf-Ganjouei A, Assadi R, Ataeinia B, Atre SR, Quintanilla BPA, Ayanore MA, Azari S, Babaee E, Babazadeh A, Badawi A, Bagheri S, Bagherzadeh M, Baheiraei N, Balouchi A, Barac A, Bassat Q, Baune BT, Bayati M, Bedi N, Beghi E, Behzadifar M, Behzadifar M, Belay YB, Bell B, Bell ML, Berbada DA, Bernstein RS, Bhattacharjee NV, Bhattarai S, Bhutta ZA, Bijani A, Bohlouli S, Breitborde NJK, Britton G, Browne AJ, Nagaraja SB, Busse R, Butt ZA, Car J, Cárdenas R, Castañeda-Orjuela CA, Cerin E, Chanie WF, Chatterjee P, Chu DT, Cooper C, Costa VM, Dalal K, Dandona L, Dandona R, Daoud F, Daryani A, Das Gupta R, Davis I, Davis Weaver N, Davitoiu DV, De Neve JW, Demeke FM, Demoz GT, Deribe K, Desai R, Deshpande A, Desyibelew HD, Dey S, Dharmaratne SD, Dhimal M, Diaz D, Doshmangir L, Duraes AR, Dwyer-Lindgren L, Earl L, Ebrahimi R, Ebrahimpour S, Effiong A, Eftekhari A, Ehsani-Chimeh E, El Sayed I, El Sayed Zaki M, El Tantawi M, El-Khatib Z, Emamian MH, Enany S, Eskandarieh S, Eyawo O, Ezalarab M, Faramarzi M, Fareed M, Faridnia R, Faro A, Fazaeli AA, Fazlzadeh M, Fentahun N, Fereshtehnejad SM, Fernandes JC, Filip I, Fischer F, Foigt NA, Foroutan M, Francis JM, Fukumoto T, Fullman N, Gallus S, Gebre DG, Gebrehiwot TT, Gebremeskel GG, Gessner BD, Geta B, Gething PW, Ghadimi R, Ghadiri K, Ghajarzadeh M, Ghashghaee A, Gill PS, Gill TK, Golding N, Gomes NGM, Gona PN, Gopalani SV, Gorini G, Goulart BNG, Graetz N, Greaves F, Green MS, Guo Y, Haj-Mirzaian A, Haj-Mirzaian A, Hall BJ, Hamidi S, Haririan H, Haro JM, Hasankhani M, Hasanpoor E, Hasanzadeh A, Hassankhani H, Hassen HY, Hegazy MI, Hendrie D, Heydarpour F, Hird TR, Hoang CL, Hollerich G, Rad EH, Hoseini-Ghahfarokhi M, Hossain N, Hosseini M, Hosseinzadeh M, Hostiuc M, Hostiuc S, Househ M, Hsairi M, Ilesanmi OS, Imani-Nasab MH, Iqbal U, Irvani SSN, Islam N, Islam SMS, Jürisson M, Balalami NJ, Jalali A, Javidnia J, Jayatilleke AU, Jenabi E, Ji JS, Jobanputra YB, Johnson K, Jonas JB, Shushtari ZJ, Jozwiak JJ, Kabir A, Kahsay A, Kalani H, Kalhor R, Karami M, Karki S, Kasaeian A, Kassebaum NJ, Keiyoro PN, Kemp GR, Khabiri R, Khader YS, Khafaie MA, Khan EA, Khan J, Khan MS, Khang YH, Khatab K, Khater A, Khater MM, Khatony A, Khazaei M, Khazaei S, Khazaei-Pool M, Khubchandani J, Kianipour N, Kim YJ, Kimokoti RW, Kinyoki DK, Kisa A, Kisa S, Kolola T, Kosen S, Koul PA, Koyanagi A, Kraemer MUG, Krishan K, Krohn KJ, Kugbey N, Kumar GA, Kumar M, Kumar P, Kuupiel D, Lacey B, Lad SD, Lami FH, Larsson AO, Lee PH, Leili M, Levine AJ, Li S, Lim LL, Listl S, Longbottom J, Lopez JCF, Lorkowski S, Magdeldin S, Abd El Razek HM, Abd El Razek MM, Majeed A, Maleki A, Malekzadeh R, Malta DC, Mamun AA, Manafi N, Manda AL, Mansourian M, Martins-Melo FR, Masaka A, Massenburg BB, Maulik PK, Mayala BK, Mazidi M, McKee M, Mehrotra R, Mehta KM, Meles GG, Mendoza W, Menezes RG, Meretoja A, Meretoja TJ, Mestrovic T, Miller TR, Miller-Petrie MK, Mills EJ, Milne GJ, Mini GK, Mir SM, Mirjalali H, Mirrakhimov EM, Mohamadi E, Mohammad DK, Darwesh AM, Mezerji NMG, Mohammed AS, Mohammed S, Mokdad AH, Molokhia M, Monasta L, Moodley Y, Moosazadeh M, Moradi G, Moradi M, Moradi Y, Moradi-Lakeh M, Moradinazar M, Moraga P, Morawska L, Mosapour A, Mousavi SM, Mueller UO, Muluneh AG, Mustafa G, Nabavizadeh B, Naderi M, Nagarajan AJ, Nahvijou A, Najafi F, Nangia V, Ndwandwe DE, Neamati N, Negoi I, Negoi RI, Ngunjiri JW, Thi Nguyen HL, Nguyen LH, Nguyen SH, Nielsen KR, Ningrum DNA, Nirayo YL, Nixon MR, Nnaji CA, Nojomi M, Noroozi M, Nosratnejad S, Noubiap JJ, Motlagh SN, Ofori-Asenso R, Ogbo FA, Oladimeji KE, Olagunju AT, Olfatifar M, Olum S, Olusanya BO, Oluwasanu MM, Onwujekwe OE, Oren E, Ortega-Altamirano DDV, Ortiz A, Osarenotor O, Osei FB, Osgood-Zimmerman AE, Otstavnov SS, Owolabi MO, P A M, Pagheh AS, Pakhale S, Panda-Jonas S, Pandey A, Park EK, Parsian H, Pashaei T, Patel SK, Pepito VCF, Pereira A, Perkins S, Pickering BV, Pilgrim T, Pirestani M, Piroozi B, Pirsaheb M, Plana-Ripoll O, Pourjafar H, Puri P, Qorbani M, Quintana H, Rabiee M, Rabiee N, Radfar A, Rafiei A, Rahim F, Rahimi Z, Rahimi-Movaghar V, Rahimzadeh S, Rajati F, Raju SB, Ramezankhani A, Ranabhat CL, Rasella D, Rashedi V, Rawal L, Reiner RC, Renzaho AMN, Rezaei S, Rezapour A, Riahi SM, Ribeiro AI, Roever L, Roro EM, Roser M, Roshandel G, Roshani D, Rostami A, Rubagotti E, Rubino S, Sabour S, Sadat N, Sadeghi E, Saeedi R, Safari Y, Safari-Faramani R, Safdarian M, Sahebkar A, Salahshoor MR, Salam N, Salamati P, Salehi F, Zahabi SS, Salimi Y, Salimzadeh H, Salomon JA, Sambala EZ, Samy AM, Santric Milicevic MM, Jose BPS, Saraswathy SYI, Sarmiento-Suárez R, Sartorius B, Sathian B, Saxena S, Sbarra AN, Schaeffer LE, Schwebel DC, Sepanlou SG, Seyedmousavi S, Shaahmadi F, Shaikh MA, Shams-Beyranvand M, Shamshirian A, Shamsizadeh M, Sharafi K, Sharif M, Sharif-Alhoseini M, Sharifi H, Sharma J, Sharma R, Sheikh A, Shields C, Shigematsu M, Shiri R, Shiue I, Shuval K, Siddiqi TJ, Silva JP, Singh JA, Sinha DN, Sisay MM, Sisay S, Sliwa K, Smith DL, Somayaji R, Soofi M, Soriano JB, Sreeramareddy CT, Sudaryanto A, Sufiyan MB, Sykes BL, Sylaja PN, Tabarés-Seisdedos R, Tabb KM, Tabuchi T, Taveira N, Temsah MH, Terkawi AS, Tessema ZT, Thankappan KR, Thirunavukkarasu S, To QG, Tovani-Palone MR, Tran BX, Tran KB, Ullah I, Usman MS, Uthman OA, Vahedian-Azimi A, Valdez PR, van Boven JFM, Vasankari TJ, Vasseghian Y, Veisani Y, Venketasubramanian N, Violante FS, Vladimirov SK, Vlassov V, Vos T, Vu GT, Vujcic IS, Waheed Y, Wakefield J, Wang H, Wang Y, Wang YP, Ward JL, Weintraub RG, Weldegwergs KG, Weldesamuel GT, Westerman R, Wiysonge CS, Wondafrash DZ, Woyczynski L, Wu AM, Xu G, Yadegar A, Yamada T, Yazdi-Feyzabadi V, Yilgwan CS, Yip P, Yonemoto N, Lebni JY, Younis MZ, Yousefifard M, Yousof HSA, Yu C, Yusefzadeh H, Zabeh E, Moghadam TZ, Bin Zaman S, Zamani M, Zandian H, Zangeneh A, Zerfu TA, Zhang Y, Ziapour A, Zodpey S, Murray CJL, Hay SI

Abstract
Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2-to end preventable child deaths by 2030-we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000-2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations.

PMID: 31619795 [PubMed - indexed for MEDLINE]

Re: "Outpatient Breastfeeding Champion Program: Breastfeeding Support in Primary Care" by Patterson et al. (Breastfeed Med 2020;15(1):44-48. DOI: 10.1089/bfm.2019.0108).

March 27, 2020
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Re: "Outpatient Breastfeeding Champion Program: Breastfeeding Support in Primary Care" by Patterson et al. (Breastfeed Med 2020;15(1):44-48. DOI: 10.1089/bfm.2019.0108).

Breastfeed Med. 2020 Mar 25;:

Authors: Debnath F, Chakraborty D, Deb AK

PMID: 32208928 [PubMed - as supplied by publisher]

Emergence of Haitian variant genotype and altered drug susceptibility in Vibrio cholerae O1 El Tor-associated cholera outbreaks in Solapur, India.

March 26, 2020
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Emergence of Haitian variant genotype and altered drug susceptibility in Vibrio cholerae O1 El Tor-associated cholera outbreaks in Solapur, India.

Int J Antimicrob Agents. 2020 Mar;55(3):105853

Authors: Kumar P, Yadav P, Ingole KV, Jaiswal RK, Khalid NS, Deshmukh DG, Goel AK, Yadava PK

Abstract
It is evident from previous cholera epidemics/outbreaks in India, Africa and America that isolates of the seventh pandemic Vibrio cholerae El Tor (7PET) with Haitian cholera toxin (HCT) genotype were associated with increased mortality. The present study highlights the emergence of 7PET-HCT isolates causing two cholera outbreaks in Walsang and Wagdari (Solapur, India) in 2016. Molecular analyses revealed that 7PET strains from earlier outbreaks (2010 and 2012) were progenitors of the current 7PET-HCT isolates. Isolates from the 2016 outbreaks carried qnrVC and floR genes and showed reduced susceptibility to tetracycline, ciprofloxacin and azithromycin, drugs recommended by the World Health Organization (WHO) for the treatment of cholera. Remarkably, protein profiling and mass spectrometry revealed disappearance of the outer membrane protein U (OmpU) porin in 7PET-HCT isolates from the second outbreak in 2016. Downregulation of ompU gene expression was also confirmed at the transcriptional level. Strains with downregulated OmpU showed reduced minimum inhibitory concentrations (MICs) for polymyxin B, which is a pore-forming antimicrobial agent. A multipronged approach is of utmost importance to prevent further spread of circulating 7PET-HCT strains. There is a pressing need for the formulation and implementation of international policies to closely monitor the effective use of antibiotics in order to prevent the further rise and spread of antimicrobial resistance.

PMID: 31770631 [PubMed - indexed for MEDLINE]

Post-monsoon waterlogging-associated upsurge of cholera cases in and around Kolkata metropolis, 2015.

March 26, 2020
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Post-monsoon waterlogging-associated upsurge of cholera cases in and around Kolkata metropolis, 2015.

Epidemiol Infect. 2019 01;147:e167

Authors: Mukhopadhyay AK, Deb AK, Chowdhury G, Debnath F, Samanta P, Saha RN, Manna B, Bhattacharya MK, Datta D, Okamoto K, Bhadra UK, Dutta S

Abstract
The Infectious Diseases and Beliaghata General Hospital, Kolkata, India witnessed a sudden increase in admissions of diarrhoea cases during the first 2 weeks of August 2015 following heavy rainfall. This prompted us to investigate the event. Cases were recruited through hospital-based surveillance along with the collection of socio-demographic characteristics and clinical profile using a structured questionnaire. Stool specimens were tested at bacteriological laboratory of the National Institute of Cholera and Enteric Diseases (NICED), Kolkata. Admission of 3003 diarrhoea cases, clearly indicated occurrence of outbreak in Kolkata municipal area as it was more than two standard deviation of the mean number (911; s.d. = 111) of diarrhoea admissions during the same period in previous 7 years. Out of 164 recruited cases, 25% were under-5 children. Organisms were isolated from 80 (49%) stool specimens. Vibrio cholerae O1 was isolated from 50 patients. Twenty-eight patients had this organism as the sole pathogen. Among 14 infants, five had cholera. All V. cholerae O1 isolates were resistant to nalidixic acid, followed by co-trimoxazole (96%), streptomycin (92%), but sensitive to fluroquinolones. We confirmed the occurrence of a cholera outbreak in Kolkata during August 2015 due to V. cholerae O1 infection, where infants were affected.

PMID: 31063116 [PubMed - indexed for MEDLINE]

Nonlinear two-point boundary value problems: applications to a cholera epidemic model.

March 24, 2020
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Nonlinear two-point boundary value problems: applications to a cholera epidemic model.

Proc Math Phys Eng Sci. 2020 Feb;476(2234):20190673

Authors: Chowdhury A, Tanveer S, Wang X

Abstract
This paper is concerned primarily with constructive mathematical analysis of a general system of nonlinear two-point boundary value problem when an empirically constructed candidate for an approximate solution (quasi-solution) satisfies verifiable conditions. A local analysis in a neighbour- hood of a quasi-solution assures the existence and uniqueness of solutions and, at the same time, provides error bounds for approximate solutions. Applying this method to a cholera epidemic model, we obtain an analytical approximation of the steady-state solution with rigorous error bounds that also displays dependence on a parameter. In connection with this epidemic model, we also analyse the basic reproduction number, an important threshold quantity in the epidemiology context. Through a complex analytic approach, we determine the principal eigenvalue to be real and positive in a range of parameter values.

PMID: 32201479 [PubMed]

[Genomic recombination of the vibrio cholerae serogroup O1 El Tor pandemic strains].

March 24, 2020
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[Genomic recombination of the vibrio cholerae serogroup O1 El Tor pandemic strains].

Zhonghua Yu Fang Yi Xue Za Zhi. 2020 Mar 06;54(3):301-305

Authors: Li ZP, Pang B, Lu X, Kan B

Abstract
Objective: To analyze the genomic recombination of the vibrio cholerae serogroup O1 El Tor pandemic strains. Methods: A total of 292 complete or draft genome sequences of Vibrio cholerae O1 serogroup El Tor strains isolated from 1937 to 2015 were selected from National Biotechnology Information Center database. The genome alignment of strains was computed by snippy software by using N16961 as reference sequence. Then ClonalFrameML software was used to do the recombinant analysis. The wilcox.test function in agricolae package was used to compare the number recombinant segments and the total length of recombinant regions between small and large chromosomes. The kruskal function was used to compare the number recombinant segments and the total length of recombinant regions among different isolation continents. The KOBAS tool was used to do the gene ontology enrichment analysis of recombinant hotspot genes. Results: Of all 292 strains of Vibrio cholerae, 163 strains (55.8%) were recombined. The median of normalized recombinant segment number of small chromosome was 4.7×10(-6) (9.3×10(-7), 2.0×10(-5)), which was significantly larger than that of large chromosome [2.4×10(-6) (3.4×10(-7), 5.7×10(-6))] (P<0.001). The median (P(25),P(75)) of recombinant segment number of strains isolated from Africa, Asia, Europe, North America and South America were 23(1.0,33.0), 1.0(0.0,34.0), 6.0(2.0,13.0), 0.0(0.0,1.0) and 29.5(6.8,56.8), respectively, and the difference was statistically significant (P<0.001). The median (P(25),P(75)) of total length of recombinant regions of strains isolated from Africa, Asia, Europe, North America and South America were 233.0(4.0, 461.0), 11.0(0.0, 695.5), 56.0(4.0,111.0), 0.0(0.0,9.0) and 347.5(132.8,1 323.5) bp, respectively, and the difference was statistically significant (P<0.001). Gene ontology Enrichment analysis showed that the functions of 62 recombinant hotspot genes were mainly enrichment in chemotaxis, taxis, response to external stimulus, receptor activity and molecular transducer activity. Conclustion: In this study, we found that there were significant differences in the number of recombinant fragments and the length of recombinant regions between large and small chromosomes of Vibrio cholerae El Tor. We also found significant differences in the number of recombinant fragments and the total length of recombinant regions among different continents.

PMID: 32187936 [PubMed - indexed for MEDLINE]

Wave 2 strains of atypical Vibrio cholerae El Tor caused the 2009-2011 cholera outbreak in Papua New Guinea.

March 24, 2020
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Wave 2 strains of atypical Vibrio cholerae El Tor caused the 2009-2011 cholera outbreak in Papua New Guinea.

Microb Genom. 2019 03;5(3):

Authors: Greenhill AR, Mutreja A, Bulach D, Belousoff MJ, Jonduo MH, Collins DA, Kas MP, Wapling J, Seemann T, Lafana A, Dougan G, Brown MV, Horwood PF

Abstract
Vibrio cholerae is the causative agent of cholera, a globally important human disease for at least 200 years. In 2009-2011, the first recorded cholera outbreak in Papua New Guinea (PNG) occurred. We conducted genetic and phenotypic characterization of 21 isolates of V. cholerae, with whole-genome sequencing conducted on 2 representative isolates. The PNG outbreak was caused by an atypical El Tor strain harbouring a tandem repeat of the CTX prophage on chromosome II. Whole-genome sequence data, prophage structural analysis and the absence of the SXT integrative conjugative element was indicative that the PNG isolates were most closely related to strains previously isolated in South-East and East Asia with affiliations to global wave 2 strains. This finding suggests that the cholera outbreak in PNG was caused by an exotic (non-endemic) strain of V. cholerae that originated in South-East Asia.

PMID: 30810520 [PubMed - indexed for MEDLINE]

The Cholera Epidemics in Hamburg and What to Learn for COVID-19 (SARS-CoV-2).

March 19, 2020
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The Cholera Epidemics in Hamburg and What to Learn for COVID-19 (SARS-CoV-2).

Cytometry A. 2020 Mar 18;:

Authors: Tárnok A

Abstract
  © 2020 International Society for Advancement of Cytometry.

PMID: 32187818 [PubMed - as supplied by publisher]

Mitigating Cholera in the Aftermath of Cyclone Idai.

March 19, 2020
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Mitigating Cholera in the Aftermath of Cyclone Idai.

Am J Trop Med Hyg. 2019 11;101(5):960-962

Authors: Chen WH, Azman AS

Abstract
Catastrophic damage and floods followed the deadliest cyclone on record for the Southern Hemisphere. In the aftermath of Cyclone Idai, a cholera outbreak was detected. The global stockpile of oral cholera vaccine was rapidly deployed to counter this fast-growing epidemic. We urge the international community to continue to highlight the importance of water, sanitation, and hygiene as the long-term goal for controlling cholera and meeting the 2030 Sustainable Development Goals.

PMID: 31333158 [PubMed - indexed for MEDLINE]

The effect of climate change on cholera disease: The road ahead using artificial neural network.

March 18, 2020
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The effect of climate change on cholera disease: The road ahead using artificial neural network.

PLoS One. 2019;14(11):e0224813

Authors: Asadgol Z, Mohammadi H, Kermani M, Badirzadeh A, Gholami M

Abstract
Climate change has been described to raise outbreaks of water-born infectious diseases and increases public health concerns. This study aimed at finding out these impacts on cholera infections by using Artificial Neural Networks (ANNs) from 2021 to 2050. Daily data for cholera infection cases in Qom city, which is located in the center of Iran, were analyzed from 1998 to 2016. To determine the best lag time and combination of inputs, Gamma Test (GT) was applied. General circulation model outputs were utilized to project future climate pattern under two scenarios of Representative Concentration Pathway (RCP2.6 and RCP8.5). Statistical downscaling was done to produce high-resolution synthetic time series weather dataset. ANNs were applied for simulating the impact of climate change on cholera. The observed climate variables including maximum and minimum temperatures and precipitation were tagged as predictors in ANNs. Cholera cases were considered as the target outcome variable. Projected future (2020-2050) climate in previous step was carried out to assess future cholera incidence. A seasonal trend in cholera infection was seen. Our results elucidated that the best lag time was 21 days. According to the results of downscaling tool, future climate in the study area by 2050 will be warmer and wetter. Simulation of cholera cases indicated that there is a clear trend of increasing cholera cases under the worst scenario (RCP8.5) by the year 2050 and the highest cholera cases observe in warmer months. The precipitation was recognized as the most effective input variable by sensitivity analysis. We observed a significant correlation between low precipitation and cholera infection. There is a strong evidence to show that cholera disease is correlated with environment variables, as low precipitation and high temperatures in warmer months could provide the swifter bacterial replication. These conditions in Iran, especially in the central parts, may raise the cholera infection rates. Furthermore, ANNs is an executive tool to simulate the impact of climate change on cholera to estimate the future trend of cholera incidence for adopting protective measures in endemic areas.

PMID: 31693708 [PubMed - indexed for MEDLINE]

Public health round-up.

March 17, 2020
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Public health round-up.

Bull World Health Organ. 2019 May 01;97(5):312-313

Authors:

PMID: 31551625 [PubMed - indexed for MEDLINE]

Alternative observational designs to estimate the effectiveness of one dose of oral cholera vaccine in Lusaka, Zambia.

March 14, 2020
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Alternative observational designs to estimate the effectiveness of one dose of oral cholera vaccine in Lusaka, Zambia.

Epidemiol Infect. 2020 Mar 13;:1-18

Authors: Ferreras E, Blake A, Chewe O, Mwaba J, Zulu G, Poncin M, Rakesh A, Page AL, Quilici ML, Azman AS, Cohuet S, Ciglenecki I, Malama K, Chizema-Kawesha E, Luquero FJ

PMID: 32167038 [PubMed - as supplied by publisher]

A population model for the 2017/18 listeriosis outbreak in South Africa.

March 13, 2020

A population model for the 2017/18 listeriosis outbreak in South Africa.

PLoS One. 2020;15(3):e0229901

Authors: Witbooi PJ, Africa C, Christoffels A, Ahmed IHI

Abstract
We introduce a compartmental model of ordinary differential equations for the population dynamics of listeriosis, and we derive a model for analysing a listeriosis outbreak. The model explicitly accommodates neonatal infections. Similarly as is common in cholera modeling, we include a compartment to represent the reservoir of bacteria. We also include a compartment to represent the incubation phase. For the 2017/18 listeriosis outbreak that happened in South Africa, we calculate the time pattern and intensity of the force of infection, and we determine numerical values for some of the parameters in the model. The model is calibrated using South African data, together with existing data in the open literature not necessarily from South Africa. We make projections on the future outlook of the epidemiology of the disease and the possibility of eradication.

PMID: 32163438 [PubMed - as supplied by publisher]

Direct transmission via households informs models of disease and intervention dynamics in cholera.

March 13, 2020

Direct transmission via households informs models of disease and intervention dynamics in cholera.

PLoS One. 2020;15(3):e0229837

Authors: Meszaros VA, Miller-Dickson MD, Baffour-Awuah F, Almagro-Moreno S, Ogbunugafor CB

Abstract
While several basic properties of cholera outbreaks are common to most settings-the pathophysiology of the disease, the waterborne nature of transmission, and others-recent findings suggest that transmission within households may play a larger role in cholera outbreaks than previously appreciated. Important features of cholera outbreaks have long been effectively modeled with mathematical and computational approaches, but little is known about how variation in direct transmission via households may influence epidemic dynamics. In this study, we construct a mathematical model of cholera that incorporates transmission within and between households. We observe that variation in the magnitude of household transmission changes multiple features of disease dynamics, including the severity and duration of outbreaks. Strikingly, we observe that household transmission influences the effectiveness of possible public health interventions (e.g. water treatment, antibiotics, vaccines). We find that vaccine interventions are more effective than water treatment or antibiotic administration when direct household transmission is present. Summarizing, we position these results within the landscape of existing models of cholera, and speculate on its implications for epidemiology and public health.

PMID: 32163436 [PubMed - as supplied by publisher]

Vibrio cholerae strains with inactivated cqsS gene overproduce autoinducer-2 which enhances resuscitation of dormant environmental V. cholerae.

March 12, 2020
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Vibrio cholerae strains with inactivated cqsS gene overproduce autoinducer-2 which enhances resuscitation of dormant environmental V. cholerae.

PLoS One. 2019;14(10):e0223226

Authors: Naser IB, Hoque MM, Faruque SN, Kamruzzaman M, Yamasaki S, Faruque SM

Abstract
BACKGROUND: Toxigenic Vibrio cholerae resides in aquatic reservoirs of cholera-endemic areas mostly in a dormant form known as conditionally viable environmental cells (CVEC) in which the bacteria remain embedded in an exopolysaccharide matrix, and fail to grow in routine bacteriological culture. The CVEC can be resuscitated by supplementing culture media with either of two autoinducers CAI-1 and AI-2, which are signal molecules controlling quorum sensing, a regulatory network of bacterial gene expression dependent on cell density. This study investigated possible existence of variant strains that overproduce AIs, sufficient to resuscitate CVEC in environmental waters.
METHODS: Environmental V. cholerae isolates and Tn insertion mutants of a V. cholerae strain C6706 were screened for production of AIs using bioluminescent reporter strains. Relevant mutations in environmental strains which overproduced AI-2 were characterized by nucleotide sequencing and genetic complementation studies. Effect of AIs produced in culture supernatants of relevant strains on reactivation of CVEC in water was determined by resuscitation assays.
RESULTS: Two of 54 environmental V. cholerae isolates were found to overproduce AI-2. Screening of a Tn-insertion library of V. cholerae strain C6706, identified a mutant which overproduced AI-2, and carried Tn insertion in the cqsS gene. Nucleotide sequencing also revealed mutations inactivating the cqsS gene in environmental isolates which overproduced AI-2, and this property was reversed when complemented with a wild type cqsS gene. Culture of river water samples supplemented with spent medium of these mutants resuscitated dormant V. cholerae cells in water.
SIGNIFICANCE: V. cholerae strains with inactivated cqsS gene may offer a convenient source of AI-2 in enhanced assays for monitoring bacteriological quality of water. The results also suggest a potential role of naturally occurring cqsS mutants in the environmental biology of V. cholerae. Furthermore, similar phenomenon may have relevance in the ecology of other waterborne bacterial pathogens beyond V. cholerae.

PMID: 31574121 [PubMed - indexed for MEDLINE]

Rapid Forecasting of Cholera Risk in Mozambique: Translational Challenges and Opportunities.

March 10, 2020
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Rapid Forecasting of Cholera Risk in Mozambique: Translational Challenges and Opportunities.

Prehosp Disaster Med. 2019 Oct;34(5):557-562

Authors: Kahn R, Mahmud AS, Schroeder A, Aguilar Ramirez LH, Crowley J, Chan J, Buckee CO

Abstract
Disasters, such as cyclones, create conditions that increase the risk of infectious disease outbreaks. Epidemic forecasts can be valuable for targeting highest risk populations before an outbreak. The two main barriers to routine use of real-time forecasts include scientific and operational challenges. First, accuracy may be limited by availability of data and the uncertainty associated with the inherently stochastic processes that determine when and where outbreaks happen and spread. Second, even if data are available, the appropriate channels of communication may prevent their use for decision making.In April 2019, only six weeks after Cyclone Idai devastated Mozambique's central region and sparked a cholera outbreak, Cyclone Kenneth severely damaged northern areas of the country. By June 10, a total of 267 cases of cholera were confirmed, sparking a vaccination campaign. Prior to Kenneth's landfall, a team of academic researchers, humanitarian responders, and health agencies developed a simple model to forecast areas at highest risk of a cholera outbreak. The model created risk indices for each district using combinations of four metrics: (1) flooding data; (2) previous annual cholera incidence; (3) sensitivity of previous outbreaks to the El Niño-Southern Oscillation cycle; and (4) a diffusion (gravity) model to simulate movement of infected travelers. As information on cases became available, the risk model was continuously updated. A web-based tool was produced, which identified highest risk populations prior to the cyclone and the districts at-risk following the start of the outbreak.The model prior to Kenneth's arrival using the metrics of previous incidence, projected flood, and El Niño sensitivity accurately predicted areas at highest risk for cholera. Despite this success, not all data were available at the scale at which the vaccination campaign took place, limiting the model's utility, and the extent to which the forecasts were used remains unclear. Here, the science behind these forecasts and the organizational structure of this collaborative effort are discussed. The barriers to the routine use of forecasts in crisis settings are highlighted, as well as the potential for flexible teams to rapidly produce actionable insights for decision making using simple modeling tools, both before and during an outbreak.

PMID: 31477186 [PubMed - indexed for MEDLINE]

Spatiotemporal Patterns of Cholera Hospitalization in Vellore, India.

March 6, 2020
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Spatiotemporal Patterns of Cholera Hospitalization in Vellore, India.

Int J Environ Res Public Health. 2019 11 02;16(21):

Authors: Venkat A, Falconi TMA, Cruz M, Hartwick MA, Anandan S, Kumar N, Ward H, Veeraraghavan B, Naumova EN

Abstract
Systematically collected hospitalization records provide valuable insight into disease patterns and support comprehensive national infectious disease surveillance networks. Hospitalization records detailing patient's place of residence (PoR) can be utilized to better understand a hospital's case load and strengthen surveillance among mobile populations. This study examined geographic patterns of patients treated for cholera at a major hospital in south India. We abstracted 1401 laboratory-confirmed cases of cholera between 2000-2014 from logbooks and electronic health records (EHRs) maintained by the Christian Medical College (CMC) in Vellore, Tamil Nadu, India. We constructed spatial trend models and identified two distinct clusters of patient residence-one around Vellore (836 records (61.2%)) and one in Bengal (294 records (21.5%)). We further characterized differences in peak timing and disease trend among these clusters to identify differences in cholera exposure among local and visiting populations. We found that the two clusters differ by their patient profiles, with patients in the Bengal cluster being most likely older males traveling to Vellore. Both clusters show well-aligned seasonal peaks in mid-July, only one week apart, with similar downward trend and proportion of predominant O1 serotype. Large hospitals can thus harness EHRs for surveillance by utilizing patients' PoRs to study disease patterns among resident and visitor populations.

PMID: 31684018 [PubMed - indexed for MEDLINE]

Cholera hotspots and surveillance constraints contributing to recurrent epidemics in Tanzania.

March 6, 2020
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Cholera hotspots and surveillance constraints contributing to recurrent epidemics in Tanzania.

BMC Res Notes. 2019 Oct 21;12(1):664

Authors: Hounmanou YMG, Mølbak K, Kähler J, Mdegela RH, Olsen JE, Dalsgaard A

Abstract
OBJECTIVE: We described the dynamics of cholera in Tanzania between 2007 and 2017 and assessed the weaknesses of the current surveillance system in providing necessary data in achieving the global roadmap to 2030 for cholera control.
RESULTS: The Poisson-based spatial scan identified cholera hotspots in mainland Tanzania. A zero-inflated Poisson regression investigated the relationship between the incidence of cholera and available demographic, socio-economic and climatic exposure variables. Four cholera hotspots were detected covering 17 regions, home to 28 million people, including the central regions and those surrounding the Lakes Victoria, Tanganyika and Nyaza. The risk of experiencing cholera in these regions was up to 2.9 times higher than elsewhere in the country. Regression analyses revealed that every 100 km of water perimeter in a region increased the cholera incidence by 1.5%. Due to the compilation of surveillance data at regional level rather than at district, we were unable to reliably identify any other significant risk factors and specific hotspots. Cholera high-risk populations in Tanzania include those living near lakes and central regions. Successful surveillance require disaggregated data available weekly and at district levels in order to serve as data for action to support the roadmap for cholera control.

PMID: 31639037 [PubMed - indexed for MEDLINE]

Associations between Public Awareness, Local Precipitation, and Cholera in Yemen in 2017.

March 6, 2020
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Associations between Public Awareness, Local Precipitation, and Cholera in Yemen in 2017.

Am J Trop Med Hyg. 2019 09;101(3):521-524

Authors: Zhao S, Musa SS, Qin J, He D

Abstract
In 2017-18, a large-scale cholera outbreak swept Yemen. We calculated the number of culture-confirmed cases from the suspected cases and diagnosis testing records. We estimate 184,248 confirmed cholera cases between April 2017 and the end of 2017, and the reproduction number of 2.2 with 95% CI of [2.1, 2.3] during the initial stage. We find a significantly (nonlinear) positive association between the reproduction number (R t) and precipitation, explained 13% of transmissibility changes, with one unit (mm) increment in precipitation leading to an increment of 20.1% in R t. We find a significantly (nonlinear) negative association between the R t and cumulative Google Trends index (GTI), explained 62% of transmissibility changes, with one unit increment in cumulative GTI leading to a drop of 0.03% in R t.

PMID: 31333154 [PubMed - indexed for MEDLINE]

Vaccines for enteric diseases.

March 3, 2020
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Vaccines for enteric diseases.

Hum Vaccin Immunother. 2019;15(6):1205-1214

Authors: Cohen D, Muhsen K

PMID: 31291174 [PubMed - indexed for MEDLINE]

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