Citation: Dumisani Chirambo. Increasing the value of climate finance in an uncertain environment: Diaspora financial resources as a source of climate finance for Sub-Saharan Africa[J]. AIMS Environmental Science, 2017, 4(6): 730-742. doi: 10.3934/environsci.2017.6.730
[1] | Zishuo Yan, Hai Qi, Yueheng Lan . The role of geometric features in a germinal center. Mathematical Biosciences and Engineering, 2022, 19(8): 8304-8333. doi: 10.3934/mbe.2022387 |
[2] | Mika Yoshida, Kinji Fuchikami, Tatsuya Uezu . Realization of immune response features by dynamical system models. Mathematical Biosciences and Engineering, 2007, 4(3): 531-552. doi: 10.3934/mbe.2007.4.531 |
[3] | Jinhu Xu, Yicang Zhou . Bifurcation analysis of HIV-1 infection model with cell-to-cell transmission and immune response delay. Mathematical Biosciences and Engineering, 2016, 13(2): 343-367. doi: 10.3934/mbe.2015006 |
[4] | Qian Ding, Jian Liu, Zhiming Guo . Dynamics of a malaria infection model with time delay. Mathematical Biosciences and Engineering, 2019, 16(5): 4885-4907. doi: 10.3934/mbe.2019246 |
[5] | Ting Guo, Zhipeng Qiu . The effects of CTL immune response on HIV infection model with potent therapy, latently infected cells and cell-to-cell viral transmission. Mathematical Biosciences and Engineering, 2019, 16(6): 6822-6841. doi: 10.3934/mbe.2019341 |
[6] | Yan Wang, Minmin Lu, Daqing Jiang . Viral dynamics of a latent HIV infection model with Beddington-DeAngelis incidence function, B-cell immune response and multiple delays. Mathematical Biosciences and Engineering, 2021, 18(1): 274-299. doi: 10.3934/mbe.2021014 |
[7] | A. M. Elaiw, Raghad S. Alsulami, A. D. Hobiny . Global dynamics of IAV/SARS-CoV-2 coinfection model with eclipse phase and antibody immunity. Mathematical Biosciences and Engineering, 2023, 20(2): 3873-3917. doi: 10.3934/mbe.2023182 |
[8] | Katrine O. Bangsgaard, Morten Andersen, Vibe Skov, Lasse Kjær, Hans C. Hasselbalch, Johnny T. Ottesen . Dynamics of competing heterogeneous clones in blood cancers explains multiple observations - a mathematical modeling approach. Mathematical Biosciences and Engineering, 2020, 17(6): 7645-7670. doi: 10.3934/mbe.2020389 |
[9] | Huan Kong, Guohong Zhang, Kaifa Wang . Stability and Hopf bifurcation in a virus model with self-proliferation and delayed activation of immune cells. Mathematical Biosciences and Engineering, 2020, 17(5): 4384-4405. doi: 10.3934/mbe.2020242 |
[10] | Tahir Khan, Fathalla A. Rihan, Muhammad Ibrahim, Shuo Li, Atif M. Alamri, Salman A. AlQahtani . Modeling different infectious phases of hepatitis B with generalized saturated incidence: An analysis and control. Mathematical Biosciences and Engineering, 2024, 21(4): 5207-5226. doi: 10.3934/mbe.2024230 |
The production of high-affinity antibodies capable of broad neutralization, viral inactivation, and protection against viral infections or disease requires activation, expansion, and maturation of B-cells into virus specific long-lived plasma and memory cells [46]. Germinal centers (GC) are the anatomical structures in which B-cells undergo somatic hypermutation, immunoglobulin class switching, and antigen-specific selection [30]. Somatic hypermutations are random and, therefore, the emergence of non-autoreactive, high-affinity B-cell clones requires strong selection through competition for survival signals [48]. The exact nature of these survival signals is poorly understood. T follicular helper (Tfh) cells have been identified as an important factor in driving B-cell hypermutation inside germinal centers [45]. Indeed, recent experiments have identified correlations between the density, function, and infection status of Tfh cells and the development of mature germinal centers [18,31,32,33,44,43,36].
Determining the characteristics of germinal centers such as their formation, size, and composition is important in understanding the protective mechanisms against pathogens that induce chronic infections. During HIV infections, only approximately 15-20% of chronically infected subjects develop antibodies with neutralization breadth [25,40]. These antibodies are highly mutated compared to antibodies induced by most viral infections in vivo [41] or through vaccination [26]. For example, the high-affinity human antibody VRC01, which neutralizes 90% of HIV-1, has 70-90 somatic mutations [47] compared to the natural 5-10 somatic mutations [14]. The mechanisms that allow for the production of protective antibodies in some patients but not others are still under investigation [15,39,7,16,3].
Mathematical models have been used in the past to investigate the mechanisms responsible for B-cell somatic hypermutations inside the germinal centers [10,28,22,21,6,12,19,23,1,36,34]. Early studies hypothesized that re-entry into new GCs of B-cells from previous GCs may explain the affinity maturation process [10,28]. Others showed that affinity maturation requires cyclic transition of B-cells between the two anatomical structures of the germinal center: the dark and light zones [22,21,6,12,19,23,1]. The models that incorporate dark and light zones investigated the role of molecular mechanisms such as competition for Tfh cells [23,36,34], antigen on the surface of follicular dendritic cells [37], binding sites [12], and clonal competition [35] in facilitating movement between the two zones. Lastly, they investigated internal and external stimuli that lead to germinal center termination [20,27,1]. These studies have not considered the mechanisms behind the emergence of large number of B-cell somatic hypermutations inside germinal centers as seen in some HIV patients [47]. Nor did they present hypotheses behind the absence of broadly neutralizing antibodies in the majority of HIV patients. Understanding the mechanistic interactions inside GCs that lead to production of plasma cells capable of producing antibodies with neutralization breadth forms the focus of this paper.
To address this, we develop mathematical models of germinal center formation that investigate the role of B-cell competition, Tfh cells, and antigen in inducing large numbers of B-cell somatic hypermutations, as seen in the few HIV patients that produce broadly neutralizing antibodies. We first develop a deterministic model of Tfh cell-B cell interactions to determine how B-cell selection and competition influences GC formation in acute infections. We fit the model to published germinal center B-cell data to estimate parameters. We then investigate the mechanisms that allow for emergence of highly mutated B-cell clones that are capable of protecting against chronic infections with non-mutating antigen, i.e. substances that do not mutate but stimulate antibody generation. Finally, we investigate how our predictions change when we consider antigenic mutation.
For a non-mutating pathogen, we predict that when only a few rounds of somatic hypermutations are needed for the clearance of a pathogen, as in acute infections, the Tfh cells are not limiting the emergence of high affinity B-cell clones. When large numbers of somatic hypermutations arise, however, a limitation in the number of Tfh cells may prevent B-cell clones of higher affinity from emerging and becoming the dominant B-cell population inside the germinal centers. Moreover, we predict that for a mutating pathogen which drives the somatic hypermutation of B-cells, emergence of B clones of highest affinity may be hindered not only through a limitation in the number of Tfh cells but also by the speed of the viral mutation.
We develop a mathematical model of B-Tfh cell dynamics which considers the interaction between the naive CD4 T-cells (
Primed follicular B-cells
The system describing these interactions is given by:
dNdt=sN−dNN−αNVN, | (1a) |
dHdt=αNVN−dHH−γHB0, | (1b) |
dGdt=γHB0−dGG−ηGn∑i=1Bi, | (1c) |
dB0dt=−dB0−σB0H, | (1d) |
dB1dt=ασB0H−σB1G−dB1, | (1e) |
dBidt=ασBi−1G−σBiG−dBi, | (1f) |
dBndt=ασBn−1G−dBn−κBn, | (1g) |
dPdt=κBn, | (1h) |
dVdt=−μVP, | (1i) |
for
Bt=n∑i=1Bi, | (2) |
for acute infections and for chronic infections where many rounds of affinity maturation lead to development of broadly neutralizing antibody-producing plasma cells, as seen in a few HIV infections [25,40].
In acute infections, B clones undergo between 5 and 10 steps of affinity maturation [14,29,27]. Without loss of generality, we set
The
In our model, the per capita death rates of all CD4 T-cells are equal,
B-cells in each B clone die at rate
Name | Value | Units | Description | Citation |
cells per ml per day | Naive CD4 T-cell recruitment rate | [38] | ||
per day | Naive CD4 T-cell death rate | [38] | ||
ml per day per cell | Pre-Tfh cell production rate | |||
per day | Pre-Tfh cell death rate | [38] | ||
per day | Tfh cell death rate | [38] | ||
per day | B-cell death rate | [11] | ||
per day | Plasma cells production rate | |||
per cell per day | Pre-Tfh cell differentiation rate | [36] | ||
per cell per day | Antigen removal rate | |||
per cell per day | Tfh competition rate | |||
cells per ml | Initial amount of CD4 T cells | [38] | ||
0 | cells per ml | Initial amount of Pre-Tfh cells | ||
0 | cells per ml | Initial amount of Tfh cells | ||
3 | cells | Initial amount of B-cells | [13,11] | |
0 | cells | Initial amount of B-cell clones | ||
0 | cells | Initial amount of plasma cells | ||
per ml | Initial amount of non-mutating antigen | [9] |
We estimate the remaining parameters
Name | Units | Value | Description | Confidence Intervals |
27.469 | B-cell offspring production rate | [14.015 40.924] | ||
ml per cell per day | Affinity maturation rate | [4.8 |
The dynamics of all variables of system (1) over time for parameters in Tables 1 and 2 are shown in Figure 1. The number of offspring produced by each B-cell clone is
The total number of B-cells in the germinal center,
The pre-Tfh and Tfh populations,
For
We performed a focused analysis of the time-dependent sensitivity of model (1)'s trajectories to parameter variation, known as a semi-relative sensitivity analysis. We start by looking at the sensitivity of variables
In Figure 2 we compared the semi-relative sensitivity curves
We further looked for parameters that have antagonistic effects on the
We next want to understand the size and B-cell clone compositions of germinal centers during prolonged antigenic stimuli. During chronic virus infections with viruses like HIV, the development of broadly neutralizing antibodies, with high mutation levels, can occur after many years of infection. For example, the high-affinity human antibody VRC01 has 70-90 mutations [47]. We will use model (1) and parameters in Tables 1 and 2 as a starting point for understanding how the B-cell and Tfh cell dynamics change when many rounds of somatic hypermutations are allowed. Most importantly, we want to determine the mechanistic interactions that allow for the emergence of a large enough B-clone with the highest level of mutation, which is capable of removing the antigen.
We represent highly mutated antibodies by increasing the level of admissible B-cell somatic hypermutations to
We observe that the Tfh cell population is smaller compared to the acute case during the contraction time, i.e. past
To gain an understanding on the role of competition for Tfh cell help we compute and plot the distribution of B-cell clones for
Experimental data suggests that the key to developing therapies againstchronic HIV infection lies in creating B-cells of the highest allowed level of somatic hypermutation [30]. Such later clones are instrumental for creating plasma and memory cells that produce highly mutated antibodies capable of neutralizing HIV virus. Our model is such that only the B-cells in the last clone become plasma cells that remove the virus, and since few
Not surprisingly, clone
Under the adjusted values,
Our model does not consider the effect of a mutating antigen, nor does it consider the need of both antigenic stimuli and Tfh cell help at each stage of B-cell somatic hypermutation. Previous studies predict that B-cell hypermutation is dependent on not only the ability of B-cells to recruit Tfh cell help, but also on the ability of the B-cells to retrieve and present antigen deposited on follicular dendritic cells [42,4,23,34].
We extend model (1) to account for a mutating virus. In particular, we model a sequential mutation from virus
dV0dt=−fV0−μV0P, | (3a) |
dVidt=fVi−1−fVi−μViP, | (3b) |
dVn−1dt=fVn−2−μVn−1P, | (3c) |
dNdt=sN−dNN−αϕNn−1∑i=0ViN, | (3d) |
dHdt=αϕNn−1∑i=0ViN−dHH−γHB0, | (3e) |
dGdt=γHB0−dGG−ηGn∑i=1Bi, | (3f) |
dB0dt=−d0B0−σB0HV0, | (3g) |
dB1dt=ασB0HV0−σV1B1G−dB1, | (3h) |
dBjdt=ασBj−1GVj−1−σBjVjG−dBj, | (3i) |
dBndt=ασBn−1GVn−1−dBn−κBn, | (3j) |
dPdt=κBn, | (3k) |
for
We numerically solve model (3), using parameters in Tables 1 and 2,
We look in detail at the slow mutation case. For
dBjdt=ασBj−1GVj−1−σBjVjG+rBj−dBj, | (4) |
where
Lastly, when we consider that the number of somatic hypermutations needed to produce plasma cells is
We developed a mathematical model of germinal center formation that includes competition between B-cell clones for Tfh cell stimulation. When we model responses to an acute pathogen requiring eight rounds of somatic hypermutations, the model reproduces the dynamics observed during germinal center formation, such as the size of the B-cell population, the time of germinal center termination, and the ratio between pre-Tfh and Tfh populations following antigenic challenge. We fit the model to data, and found that there are enough Tfh cells to allow for B-cell clones of the highest level of somatic hypermutations to emerge.
We then extended our model to allow for as many as
When modeling a mutating antigen that drives the rate of B-cell somatic hypermutations, plasma cell production is dependent on the speed of viral mutation. For eight rounds of somatic hypermutations, fast and intermediate mutating plasma cells capable of removing the virus are always produced. A slow mutating virus, however, requires an additional antigen-independent B-cell expansion that maintains enough B-cells inside germinal centers to induce the next round of somatic hypermutation even when the antigenic stimulus is delayed. As in the non-mutating case Tfh are not limiting the emergence of all B-cell clones. For
Our models assume that the B-cell division rate is exponentially distributed (as in [2]), and disregarded the inherent cell cycle delay shown experimentally and considered in previous modeling studies [24,5,17]. One of the reasons for this assumption is the fact that the
f(t)={0,t<τ,1,t≥τ, | (5) |
and
Our work assumes that B-cells must undergo a strict number of mutations before maturing into plasma cells. We found that modeling the breadth of the response, through creating plasma cells of different affinities at each stage of B-cell somatic hypermutations did not change our results. Further work is needed to determine the tradeoff between the need of high mutation numbers and the breadth of the immune response in fighting chronic infections.
In summary, we have developed models of Tfh-B-cell interactions to examine the dynamics of germinal centers in both acute and chronic infections. We found that T follicular helper cells are a limiting factor in the emergence of extremely high rounds of B-cell somatic hypermutations for both non-mutating and mutating virus. Moreover, we found that this limitation can be removed by inducing faster transition between clones and limiting the sizes of individual clones. Lastly, for a mutating virus that drives the somatic hypermutations, additional factors such as antigen-independent B-cell proliferation may be needed for plasma cell production and virus neutralization. These results may provide insight into the germinal center role during chronic infections.
We would like to thank the anonymous reviewers for the valuable comments and suggestions.
[1] |
Lau LC, Lee KT, Mohamed AR (2012) Global warming mitigation and renewable energy policy development from the Kyoto Protocol to the Copenhagen Accord-A comment. Renew Sust Energ Rev 16: 5280-5284. doi: 10.1016/j.rser.2012.04.006
![]() |
[2] |
Cobbinah PB, Erdiaw-Kwasie MO, Amoateng P (2015) Africa's urbanisation: implications for sustainable development. Cities 47: 62-72. doi: 10.1016/j.cities.2015.03.013
![]() |
[3] |
de Oliveira JAP (2009) The implementation of climate change related policies at the subnational level: An analysis of three countries. Habitat Int 33: 253-259. doi: 10.1016/j.habitatint.2008.10.006
![]() |
[4] |
Abadie LM, Galarraga I, Rübbelke D (2013) An analysis of the causes of the mitigation bias in international climate finance. Mitig Adapt Strat GL 18: 943-955. doi: 10.1007/s11027-012-9401-7
![]() |
[5] |
Morton TA, Rabinovich A, Marshall D, et al. (2011) The future that may (or may not) come: How framing changes responses to uncertainty in climate change communications. Global Environ Change 21: 103-109. doi: 10.1016/j.gloenvcha.2010.09.013
![]() |
[6] | Wilson RH, Smith TG, Urban resilience to climate change challenges in Africa. Working Paper No.4., The Robert S. Strauss Centre for International Security and Law, Texas. 2014. |
[7] |
Scrieciu SS, Barker T, Ackerman F (2013) Pushing the boundaries of climate economics: critical issues to consider in climate policy analysis. Ecol Econ 85: 155-165. doi: 10.1016/j.ecolecon.2011.10.016
![]() |
[8] |
Hu Y, Monroy CR (2012) Chinese energy and climate policies after Durban: Save the Kyoto Protocol. Renew Sust Energ Rev 16: 3243-3250. doi: 10.1016/j.rser.2012.02.048
![]() |
[9] |
Rong F (2010) Understanding developing country stances on post-2012 climate change negotiations: Comparative analysis of Brazil, China, India, Mexico, and South Africa. Energy Policy 38: 4582-4591. doi: 10.1016/j.enpol.2010.04.014
![]() |
[10] | Howlett M (2014) Why are policy innovations rare and so often negative? Blame avoidance and problem denial in climate change policy-making. Global Environ Change 29: 395-403. |
[11] |
Masini A, Menichetti E (2013) Investment decisions in the renewable energy sector: An analysis of nonfinancial drivers. Tech Forecast Soc Change 80: 510-524. doi: 10.1016/j.techfore.2012.08.003
![]() |
[12] | Hallegatte S, Bangalore M, Bonzanigo L, et al., Shock Waves: Managing the Impacts of Climate Change on Poverty. Climate Change and Development Series, World Bank, Washington, DC. 2016. |
[13] |
Buurman J, Babovic V (2016) Adaptation Pathways and Real Options Analysis: An approach to deep uncertainty in climate change adaptation policies. Policy Soc 35: 137-150. doi: 10.1016/j.polsoc.2016.05.002
![]() |
[14] | Buchner B, Falconer A, Hervé-Mignucci M, et al., The Landscape of Climate Finance: A CPI report. Climate Policy Initiative, Venice, 2011. Available from: http://climatepolicyinitiative.org/wp-content/uploads/2011/10/The-Landscape-of-Climate-Finance-120120.pdf. |
[15] | UN (United Nations), Transforming our world: the 2030 Agenda for Sustainable Development. United Nations, New York. 2015. |
[16] |
Glemarec Y (2012) Financing off-grid sustainable energy access for the poor. Energy Policy 47: 87-93. doi: 10.1016/j.enpol.2012.03.032
![]() |
[17] | Yu Y (2014) Climate finance, Africa and China's role. Afr East-Asian Affair 1: 36-57. |
[18] | Kato T, Ellis J, Pauw P, et al., Scaling up and replicating effective climate Finance interventions. Climate Change Expert Group Paper 2014 (1), OECD, Paris. 2014. |
[19] | Mbeva K, Ochieng C, Atela J, et al., Intended Nationally Determined Contributions as a Means to Strengthening Africa's Engagement in International Climate Negotiations. Climate Resilient Economies Working Paper no. 001/2015, African Centre for Technology Studies (ACTS), ACTS Press, Nairobi. 2015. |
[20] | Campillo G, Mullan M, Vallejo L, Climate Change Adaptation and Financial Protection: Synthesis of Key Findings from Colombia and Senegal. OECD Environment Working Papers No. 120, OECD Publishing, Paris. 2017. |
[21] | World Bank, Africa's Pulse, Oct 2015, vol. 12, World Bank, Washington, DC. 2015. |
[22] |
Eriksen S, Aldunce P, Bahinipati CS, et al. (2011) When not every response to climate change is a good one: Identifying principles for sustainable adaptation. Climate Dev 3:7-20. doi: 10.3763/cdev.2010.0060
![]() |
[23] |
Pasquini L, Cowling RM, Ziervogel G (2013) Facing the heat: Barriers to mainstreaming climate change adaptation in local government in the Western Cape Province, South Africa. Habitat Int 40: 225-232. doi: 10.1016/j.habitatint.2013.05.003
![]() |
[24] | AfDB (African Development Bank), Available from: http://www.afdb.org/en/cop/programme/climate-finance-for-africa/. |
[25] |
Mitchell TD, Hulme M (1999) Predicting regional climate change: living with uncertainty. Prog Physl Geog 23: 57-78. doi: 10.1191/030913399672023346
![]() |
[26] | UNFCCC (United Nations Framework Convention on Climate Change), Report of the conference of the parties on its sixteenth session; addendum: part two: action taken by the conference of the parties at its sixteenth session. FCCC/CP/2010/7/Add.1. 2011. |
[27] | Buchner B, Stadelmann M, Wilkinson J, et al., The Global Landscape of Climate Finance 2014. Climate Policy Initiative, Venice. 2014. |
[28] |
Zhang W, Pan X (2016) Study on the demand of climate finance for developing countries based on submitted INDC. Adv Climate Change Res 7: 99-104. doi: 10.1016/j.accre.2016.05.002
![]() |
[29] |
Marwan NF, Kadir NA, Hussin A, et al. (2013) Export, aid, remittance and growth: Evidence from Sudan. Procedia Econ Financ 7: 3-10. doi: 10.1016/S2212-5671(13)00211-6
![]() |
[30] | Centre for Environmental Policy and Advocacy, Draft Position Paper: Towards Development of Climate Change Policy in Malawi. Centre for Environmental Policy and Advocacy, Blantyre. 2012. Available from: http://www.cepa.org.mw/documents/ECRP_documents/CEPA_Draft_Position_Paper_Climate_Change_Policy.pdf. |
[31] | Bird N, Asante F, Bawakyillenuo S, et al., Public spending on climate change in Africa: Experiences from Ethiopia, Ghana, Tanzania and Uganda. Overseas Development Institute, London. 2016. |
[32] | Niang I, Ruppel OC, Abdrabo MA, et al., Africa. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part B: Regional Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1199-1265. 2014. |
[33] |
Arezki R, Brückner M (2012) Rainfall financial development, and remittances: Evidence from Sub-Saharan Africa. J Int Econ 87: 377-385. doi: 10.1016/j.jinteco.2011.12.010
![]() |
[34] | Watkins K, Quattri M, Lost in intermediation: How excessive charges undermine the benefits of remittances for Africa, Overseas Development Institute, London. 2014. |
[35] |
Kriegler E, O'Neill BC, Hallegatte S, et al. (2012) The need for and use of socio-economic scenarios for climate change analysis: A new approach based on shared socio-economic pathways. Global Environ Change 22: 807-822. doi: 10.1016/j.gloenvcha.2012.05.005
![]() |
[36] |
Anda J, Golub A, Strukova E (2009) Economics of climate change under uncertainty: Benefits of flexibility. Energy Policy 37: 1345-1355. doi: 10.1016/j.enpol.2008.11.034
![]() |
[37] |
Engels A, Hüther O, Schäfer M, et al. (2013) Public climate-change skepticism, energy preferences and political participation. Global Environ Change 23: 1018-1027. doi: 10.1016/j.gloenvcha.2013.05.008
![]() |
[38] |
Grafakos S, Flamos A, Oikonomou V, et al. (2010) Multi-criteria analysis weighting methodology to incorporate stakeholders' preferences in energy and climate policy interactions. Int J Energy Sec Manag 4: 434-461. doi: 10.1108/17506221011073851
![]() |
[39] |
Broughton EK, Brent AC, Haywood L (2012) Application of A Multi-Criteria Analysis Approach for Decision-Making in the Energy Sector: The Case of Concentrating Solar Power in South Africa. Energy Environ 23: 1221-1231. doi: 10.1260/0958-305X.23.8.1221
![]() |
[40] | Konidari P, Mavrakis D (2007) Multi-criteria evaluation of climate policy interactions. J Multi-criteria Decis Anal 14: 35-53. |
[41] |
Kalim PFHB, Shah U (2011) A multi-criteria evaluation of policy instruments for climate change mitigation in the power generation sector of Trinidad and Tobago. Energy Policy 39: 6331-6343. doi: 10.1016/j.enpol.2011.07.034
![]() |
[42] | Röser F, Day T, Kurdziel M, After Paris: What is next for Intended Nationally Determined Contributions (INDCs)? International Partnership on Mitigation and MRV and New Climate Institute, 2016. |
[43] | Day T, Röser F, Kurdziel M, Conditionality of Intended Nationally Determined Contributions (INDCs). International Partnership on Mitigation and MRV and New Climate Institute. 2016. |
[44] | Hood C, Adkins L, Levina E, Overview of INDCs Submitted by 31 August 2015. Paper No. 2015(4). Climate Change Expert Group. Organisation for Economic Co-operation and Development (OECD), Paris. 2015. |
[45] | Viljoen W (2013) Addressing climate change issue in eastern and southern Africa: EAC, COMESA, SADC and the TFTA. In Cape to Cairo: Exploring the Tripartite FTA agenda. Trade Law Centre and the Swedish Embassy, Nairobi. 1-35. |
[46] |
Mohammed YS, Mustafa MW, Bashir N (2013) Status of renewable energy consumption and developmental challenges in Sub-Sahara Africa. Renew Sust Energ Rev 27: 453-463. doi: 10.1016/j.rser.2013.06.044
![]() |
[47] |
Javadi FS, Rismanchi B, Sarraf M, et al. (2013) Global policy of rural electrification. Renew Sust Energ Rev 19: 402-416. doi: 10.1016/j.rser.2012.11.053
![]() |
[48] | International Energy Agency (IEA), Boosting the Power Sector in Sub-Saharan Africa: China's involvement. IEA, Paris. 2016. |
[49] | African Development Bank (AfDB), Sustainable Energy for All Africa Hub: Annual Report 2015–2016, African Development Bank, Abidjan, 2016. |
[50] |
Winkelman AG, Moore MR (2011) Explaining the differential distribution of Clean Development Mechanism projects across host countries. Energy Policy 39: 1132-1143. doi: 10.1016/j.enpol.2010.11.036
![]() |
[51] | Burian M, Aren C, Sterk W, et al. Integrating Africa's Least Developed Countries into the Global Carbon Market: Analyzing CDM Implementation Barriers, Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU). 2011. |
[52] | Arens C, Wang-Helmreich H, Hodes GS, et al., Assessing Support Activities by International Donors for CDM Development in Sub-Saharan Africa with Focus on Selected Least Developed Countries, Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU). 2011. |
[53] |
Afful-Koomson T (2015) The Green Climate Fund in Africa: what should be different? Climate Dev 7: 367-379. doi: 10.1080/17565529.2014.951015
![]() |
[54] |
Greening LA, Bernow S (2004) Design of coordinated energy and environmental policies: use of multi-criteria decision-making. Energy Policy 32: 721-735. doi: 10.1016/j.enpol.2003.08.017
![]() |
[55] |
McKibbin WJ, Wilcoxen PJ (2009) Uncertainty and climate change policy design. J Policy Model 31: 463-477. doi: 10.1016/j.jpolmod.2008.12.001
![]() |
[56] |
Ha S, Hale T, Ogden P (2016) Climate finance in and between developing countries: an emerging opportunity to build on. Global Policy 7: 102-108. doi: 10.1111/1758-5899.12293
![]() |
[57] | Weigel M, China's Climate Change South-South Cooperation: Track Record and Future Direction, United Nations Development Programme in China, Beijing. 2016. |
[58] | Chirambo D (2017) Enhancing Climate Change Resilience through Microfinance: Redefining the Climate Finance Paradigm to Promote Inclusive Growth in Africa. J Dev Soc 33: 1-24. |
[59] | Wibeck V (2014) Enhancing learning, communication and public engagement about climate change-some lessons from recent literature. Environ Edu Res 20: 387-411. |
[60] |
Nisbet MC (2009) Communicating Climate Change: Why Frames Matter for Public Engagement. Environ Sci Policy Sustain Dev 51: 12-23. doi: 10.3200/ENVT.51.2.12-23
![]() |
1. | Samantha Erwin, Lauren M. Childs, Stanca M. Ciupe, Mathematical model of broadly reactive plasma cell production, 2020, 10, 2045-2322, 10.1038/s41598-020-60316-8 | |
2. | Zishuo Yan, Hai Qi, Yueheng Lan, The role of geometric features in a germinal center, 2022, 19, 1551-0018, 8304, 10.3934/mbe.2022387 | |
3. | Komlan Atitey, Benedict Anchang, Mathematical Modeling of Proliferative Immune Response Initiated by Interactions Between Classical Antigen-Presenting Cells Under Joint Antagonistic IL-2 and IL-4 Signaling, 2022, 9, 2296-889X, 10.3389/fmolb.2022.777390 |
Name | Value | Units | Description | Citation |
cells per ml per day | Naive CD4 T-cell recruitment rate | [38] | ||
per day | Naive CD4 T-cell death rate | [38] | ||
ml per day per cell | Pre-Tfh cell production rate | |||
per day | Pre-Tfh cell death rate | [38] | ||
per day | Tfh cell death rate | [38] | ||
per day | B-cell death rate | [11] | ||
per day | Plasma cells production rate | |||
per cell per day | Pre-Tfh cell differentiation rate | [36] | ||
per cell per day | Antigen removal rate | |||
per cell per day | Tfh competition rate | |||
cells per ml | Initial amount of CD4 T cells | [38] | ||
0 | cells per ml | Initial amount of Pre-Tfh cells | ||
0 | cells per ml | Initial amount of Tfh cells | ||
3 | cells | Initial amount of B-cells | [13,11] | |
0 | cells | Initial amount of B-cell clones | ||
0 | cells | Initial amount of plasma cells | ||
per ml | Initial amount of non-mutating antigen | [9] |
Name | Units | Value | Description | Confidence Intervals |
27.469 | B-cell offspring production rate | [14.015 40.924] | ||
ml per cell per day | Affinity maturation rate | [4.8 |
Name | Value | Units | Description | Citation |
cells per ml per day | Naive CD4 T-cell recruitment rate | [38] | ||
per day | Naive CD4 T-cell death rate | [38] | ||
ml per day per cell | Pre-Tfh cell production rate | |||
per day | Pre-Tfh cell death rate | [38] | ||
per day | Tfh cell death rate | [38] | ||
per day | B-cell death rate | [11] | ||
per day | Plasma cells production rate | |||
per cell per day | Pre-Tfh cell differentiation rate | [36] | ||
per cell per day | Antigen removal rate | |||
per cell per day | Tfh competition rate | |||
cells per ml | Initial amount of CD4 T cells | [38] | ||
0 | cells per ml | Initial amount of Pre-Tfh cells | ||
0 | cells per ml | Initial amount of Tfh cells | ||
3 | cells | Initial amount of B-cells | [13,11] | |
0 | cells | Initial amount of B-cell clones | ||
0 | cells | Initial amount of plasma cells | ||
per ml | Initial amount of non-mutating antigen | [9] |
Name | Units | Value | Description | Confidence Intervals |
27.469 | B-cell offspring production rate | [14.015 40.924] | ||
ml per cell per day | Affinity maturation rate | [4.8 |